Available Positions

On top of the list are position descriptions that have been received by our deadline 22 December 2017. Position descriptions that have arrived after the deadline have been published 09 January 2018 at the end of this list; numbering of formerly received projects has not been changed.

Positions for summer 2018

Link to positions at (topic numbers in brackets):

School of Science (1-6) - School of Electrical Engineering (7-14) - School of Engineering (15-18) - School of Chemical Engineering (19-22) - Late arrivals (23-27)

1) Field of study: Computer Science, Engineering, Mathematics, Physics

Aalto University unit: School of Science, Department of Applied Physics

Professor in charge of the topic: Adam Foster, Patrick Rinke

Academic contact person for further information on topic: Adam Foster, adam.foster at_12pt.gif aalto.fi; Patrick Rinke, patrick.rinke at_12pt.gif aalto.fi

Title of topic: Machine Learning Strategies for Scientific Data Analysis

Short task description:                     

Scientific data can be generated through physical simulations, experimental laboratories and observations from real-world problems. Compared to just a few years ago, the advancement of scientific instruments, digital sensors and computational resources as well as storage devices have created huge collections of scientific data. Unlike traditional statistical analysis, Machine Learning (ML) thrives on growing data sets. The more data fed into an ML system, the more it can learn and apply the results to higher quality predictions and new insights. In this project, we will investigate and implement ML methods (e.g., kernel regression, Bayesian optimization, deep learning) for finding key variables influencing physical phenomena and materials properties. In particular, we will develop and exploit the wealth of materials data available (most of it generated in our research groups), and use ML to discover new materials and phenomena linked to them. Examples include investigating nano-catalysis, nanoscale microscopy imaging, molecular self-assembly, quantum phenomena, theoretical spectroscopy, and solar-energy generation.

The detailed applications and tasks will be tailored according to the background of a successful candidate. Candidates should have a basic knowledge of physics, data analysis and statistics. Knowledge of Python/MATLAB would be extremely beneficial.

2) Field of study: Quantum and statistical condensed matter physics

Aalto University unit: School of Science, Department of Applied Physics

Professor in charge of the topic: Christian Flindt

Academic contact person for further information on topic: Christian Flindt, christian.flindt at_12pt.gif aalto.fi

Title of topic: Lee-Yang zeros in condensed matter physics

Short task description:                     

We have recently developed a method that makes it possible to predict the properties of a macroscopic system based on the behavior of just a few of its constituents. The method makes use of seminal theoretical work from 1952 by the Nobel laureates Lee and Yang. Specifically, from fluctuations in small systems of thermodynamic observables such as energy or magnetization, we can determine the Lee-Yang zeros in the complex plane of the external control parameter, for instance temperature or magnetic field. By investigating the position of the Lee-Yang zeros as a function of the system size, we can predict the value of the control parameter for which a phase transition will occur in large systems. We have applied the method to classical systems that exhibit a thermal phase transition, including a molecular zipper and the Ising model in two dimensions.

In this project, we will go beyond thermal phase transitions in classical systems and investigate quantum phase transitions in interacting many-body systems. A suitable candidate will have a strong background and interest in theoretical physics, including a deep knowledge of quantum physics and statistical mechanics. Our method is rather general and the specific problem that we will work on can be tailored to the interest of the summer intern. Possible topics include Bose-Einstein condensation, many-body localization, topological phase transitions, and dynamical phase transitions in quantum many-body systems after a quench. The research will be conducted in collaboration with other members of the Quantum Transport group. Further details about our method can be found in the references below together with a link to our group home page.

-    “Experimental Determination of Dynamical Lee-Yang Zeros”,  K. Brandner, V. F. Maisi, J. P. Pekola, J. P. Garrahan, and C. Flindt, Phys. Rev. Lett. 118, 180601 (2017)
-    “Lee-Yang zeros and large-deviation statistics of a molecular zipper” A. Deger, K. Brandner, and C. Flindt, Phys. Rev. E xx, xxxxxx (201x)

Link to the Quantum Transport group: http://physics.aalto.fi/en/groups/qt/

3) Field of study: Physics

Aalto University unit: School of Science, Department of Applied Physics

Professor in charge of the topic: Mikko Alava

Academic contact person for further information on topic: Mikko Alava, mikko.alava at_12pt.gif aalto.fi

Title of topic: Machine Learning Active Phases

Short task description:                     

In this project, the intern uses image-oriented Artificial Intelligenge –methods to study the dynamical patterns and phases present in active matter systems. The data is either created by simulating appropriate models or derived, in collaboration, of the experiments of the prof. Jaakko Timonen group at the same department. The student should have an active interest and preferably some knowledge with the following topics: statistical mechanics, active matter, and machine learning.

4) Field of study: Materials physics, Metallurgy

Aalto University unit: School of Science, Department of Applied Physics

Professor in charge of the topic: Filip Tuomisto

Academic contact person for further information on topic: Filip Tuomisto, filip.tuomisto at_12pt.gif aalto.fi

Title of topic: Vacancy defects and atomic-scale damage in High Entropy Alloys

Short task description:                     

Materials used in current or future energy production often face physical and chemical environments that are extremely hostile. Combinations of chemically corrosive environments, large heat loads, hard ionizing radiation fluxes and, for example, simultaneous mechanical loads, significantly shorten the lifetime of components manufactured from currently available materials

This project will focus on a novel class of single-phase solid solution metal alloys, called high entropy alloys (HEAs) when the number of alloying elements with significant molar fractions is more than five. This multi-metal approach clearly demonstrates that a breakthrough with traditional metallurgy is possible. Beyond that concept, all the possible strategies planned to improve materials and associated microstructures by design approaches are key factors to develop new classes of (functional or structural) materials with enhanced properties. The design challenges of primary interest are corrosion and embrittlement by hydrogen and other mobile impurities into the original material. HEA performance in corrosive environments is a hot topic globally, while the fundamental understanding of these types of materials from the atomic to macroscale is currently missing from the wider scientific literature.

As an AScI Summer Intern in the Antimatter and Nuclear Engineering group at the Department of Applied Physics, you would quantify and characterize defects in high-entropy alloys using positron annihilation spectroscopy methods. The ideal applicant will be highly motivated, willing to learn, have a basic understanding of materials physics, and fluency in both written and spoken English. Previous experience in the area of condensed matter physics or experimental laboratory work would be considered an added advantage, but is not obligatory.

5) Field of study: Computer Science, Computational Biology

Aalto University unit: School of Science, Department of Computer Science

Professor in charge of the topic: Pekka Orponen

Academic contact person for further information on topic: Pekka Orponen, pekka.orponen at_12pt.gif aalto.fi

Title of topic: Algorithms for the design of RNA nanostructures

Short task description:                     

The area of DNA nanotechnology (https://en.wikipedia.org/wiki/DNA_nanotechnology) employs DNA as generic building material for assembling nanoscale objects with dimensions in the order of 10-100 nanometres. For instance, our group recently demonstrated, together with a biochemistry team from Karolinska Institutet, a general technique for rendering almost arbitrary 3D wireframe designs into biomolecules folded from a single long DNA strand (http://sci.aalto.fi/en/current/news/2015-07-23/).

For several reasons, there is increasing interest in the DNA nanotechnology community to move from DNA to RNA as source material. This is however very challenging, because RNA has remarkably much richer and less well understood folding kinetics than DNA.

The topic of this internship project is to learn about the present combinatorial models of RNA folding and develop algorithmic methods for designing RNA sequences that fold into desired 2D or 3D shapes. Simulation studies will then be used to screen the proposed designs, towards a possibility of eventual validation by laboratory experiments.

The project requires familiarity with basic algorithm design techniques, facility with combinatorial thinking, and good programming skills. Previous knowledge of biomolecules is not necessary, although it is an asset. The work is performed in the context of research project “Algorithmic designs for biomolecular nanostructures (ALBION)”, funded by the Academy of Finland. For further information, please see the research group webpage at http://research.cs.aalto.fi/nc/ .

6) Field of study: Organization studies, identity construction and entrepreneurship

Aalto University unit: School of Science, Department of Industrial Engineering and Management

Professor in charge of the topic: Marina Biniari

Academic contact person for further information on topic: Marina Biniari, marina.biniari at_12pt.gif aalto.fi

Title of topic: How do professional workers construct a positive intrapreneurial identity?

Short task description:                     

In recent years, an increasing number of professional workers have adopted an “intrapreneur” role identity to describe who they are in social media. Building on identity theory, and on the positive identity and identity work literatures, in this project we seek to understand how professional workers construct a positive identity as intrapreneurs, in terms of both content and structure.
For this project, the successful candidate will
-    Collect secondary and primary data from professional workers who self-identify as intrapreneurs. The secondary data will be collected from reviewing media and web content on the “intrapreneur” role identity, and primary data will be collected from conducting 10 semi-structured interviews
-    Combine this dataset with an existing dataset of 10 semi-structured interviews already conducted
-    Analyze the combined datasets by employing qualitative methods (grounded theory data analysis)
-    Report on the findings of this study by writing up a report and through the systematic representation of the data by using tables and data structures.
The successful candidate is required to have read social psychology, or organization studies with a good understanding of the identity literature. Further, the candidate needs to have experience with qualitative data analysis methods (i.e., grounded theory methods, and the Gioia methodology), and the ability to use the Atlas software.

7) Field of study: Human–Computer Interaction

Aalto University unit: School of Electrical Engineering, Department of Communications and Networking

Professor in charge of the topic: Antti Oulasvirta

Academic contact person for further information on topic: Kashyap Todi, kashyap.todi at_12pt.gif gmail.com

Title of topic: Comparison of Different Models of Familiarity

Short task description:                     

In domains where users are exposed to large variations in designs, they often spend excess time searching for common elements (features) in familiar locations. In our recent work, we proposed familiarisation of new interfaces. By capturing user history, and modelling familiarity, the system enables restructuring of new interfaces to improve them for each user. We presented four different models of familiarity, inspired by the human visual system.

During this internship, your task will be to compare these four different models of familiarity. You will have the opportunity to conduct user studies, and analyse the results, to empirically compare and verify these models. Additionally, you can also find ways of improving upon the models, or work on the Familiariser system to make improvements to it.

You can find more details about the system and models in our paper: http://www.kashyaptodi.com/data/FamiliarisationIUI2018.pdf.
Contact Kashyap Todi (kashyap.todi at_12pt.gif gmail.com) for more information about this topic before applying.

Necessary Skills/Pre-requisites:
-    Some knowledge of, or experience with, conducting user studies, and quantitative analysis of data.
-    The Familiariser system is written in Swift programming language; to improve on the system itself, some experience with Swift will be beneficial.

8) Field of study: Human-Computer Interaction

Aalto University unit: School of Electrical Engineering, Department of Communications and Networking

Professor in charge of the topic: Antti Oulasvirta

Academic contact person for further information on topic: Kashyap Todi, kashyap.todi at_12pt.gif gmail.com

Title of topic: Evolving User Interfaces to Transition from Novice to Expert Users

Short task description:                     

Typical user interfaces (e.g. Photoshop, Word) have various graphical elements such as toolbars, widgets, and buttons. While some of these provide core (basic) functionalities, others are intended for expert users. There is a tradeoff between displaying the maximum number of functionalities vs. presenting an easy-to-use uncluttered interface. These interfaces therefore often allow some customisation, enabling users to selectively display or hide elements. However, it can be challenging for users to select and configure an optimal interface layout to match their needs.
Adaptive user interfaces can change visual layouts to adapt to given users, thus improving usability and performance. In our prior work on Familiarisation, we recorded individual user histories, and applied visual learning models to restructure graphical layouts, making new interfaces more familiar to a user, thus improving usability. Building upon the theme of adaptive interfaces, this topic explores the possibilities of automatically evolving interfaces, to support the transition from novice to expert phases. The interface could thus present the simplest version during initial uses, which is ideal for a novice user. As the user gradually learns the interface, it could evolve over time, and increase in complexity, to reveal additional functionalities without sacrificing performance.
During the internship, your task will be to build a prototype of such an ‘evolving interface’. You will combine visual learning models of familiarity, with predictive models of performance, and use this as the basis for adapting the interface. You will also have the freedom to explore your own ideas related to interface evolution, and the possibility of conducting user studies to evaluate the results.
You can find more details about our prior work on Familiarisation here: http://www.kashyaptodi.com/data/FamiliarisationIUI2018.pdf.
Contact Kashyap Todi (kashyap.todi at_12pt.gif gmail.com) for more information about this topic before applying.

Necessary Skills/Pre-requisites:
-    Programming in Swift or Objective-C, or any other suitable language.

9) Field of study: Human-Computer interaction, Cognitive science

Aalto University unit: School of Electrical Engineering, Department of Communications and Networking

Professor in charge of the topic: Antti Oulasvirta

Academic contact person for further information on topic: Jussi Jokinen, jussi.jokinen at_12pt.gif aalto.fi

Title of topic: Emotionally Rational Interaction

Short task description:                     

This work is part of a project, which investigates the role of emotion in rational decision making when using interactive technologies. The project uses reinforcement learning models to simulate adaptive interactive behaviour, and identifies touchpoints for emotion elicitation and emotion function.
Your task is to design an interactive scenario, and use the supplied model to implement the scenario. With the help of the principal investigator, you will identify the relevant emotional touchpoints in the scenario, and investigate the emotionally rational behaviour of a user, who conducts tasks in the defined scenario. The outcome of your project should be results from a simulation, but the project can also be scaled to include a user study, where the predictions are tested empirically.
Necessary skills:
-    Python programming language
Preferably but not necessarily, the applicant can demonstrate knowledge related to one or more following areas / topics:
-    Psychology of emotion, appraisal theory
-    Machine learning, especially partially observable markov decision process (POMDP)
-    Experimental research: conducting experiments, analysing data

10) Field of study: Information visualization, Human-Computer interaction, Cognitive science

Aalto University unit: School of Electrical Engineering, Department of Communications and Networking

Professor in charge of the topic: Antti Oulasvirta

Academic contact person for further information on topic: Luana Micallef, luana.micallef at_12pt.gif aalto.fi

Title of topic: Perceptual Optimization of the Visual Design of Weighted Trees

Short task description:                     

The design of visualizations, such as scatterplots, is often not given much importance. Yet our recent award-winning paper [Micallef et al., 2017] determined that visualization design could significantly affect user’s performance in completing tasks such as correlation estimation and outlier detection. This led us to devise a framework that computationally optimizes the design of scatterplots based on models of human perception and the user’s data and task requirements. We are now working with UK’s Wellcome Trust Sanger Institute to adopt this perceptual optimization method for large real-world evolution trees to help biologists identify, for instance, how a disease is spreading or how a bacterium is adapting and changing over time. With this project, we also want to advance our current otpimization methods, such that we automatically and implicitly infer the user’s intentions and requirements, and adapt the visualization design accordingly. We are thus collaborating with bioinformatics and machine learning researchers to apply artificial intelligence methods like reinforcement learning and inverse modelling to our design optimization framework.

Your task will be to design and conduct a user study that evaluates how a tree design optimized for a specific task, such as clustering, could help the user complete the task. We might also consider using eye tracking to better analyse the participants’ reasoning process. Your work will contribute to a paper we plan to submit to the prestigious Nature Methods.

Necessary skills:
- Advanced programming experience in Python and Javascript
Preferable but necessary skills:
- User study design and execution
- Information visualization design
- Data analysis

Micallef, Luana, et al. "Towards Perceptual Optimization of the Visual Design of Scatterplots." IEEE Transactions on Visualization and Computer Graphics 23.6 (2017): 1588-1599.

11) Field of study: Information visualization, Human-Computer interaction, Cognitive science

Aalto University unit: School of Electrical Engineering, Department of Communications and Networking

Professor in charge of the topic: Antti Oulasvirta

Academic contact person for further information on topic:  Luana Micallef, luana.micallef at_12pt.gif aalto.fi

Title of topic: Mapifying the Human Genome

Short task description:                     

In this project we want to determine whether the axis of the entire genome (3 billion bases arranged across 23 pairs of chromosomes) can be mapped on a 2-dimensional space (as in e.g., [Gansner et al., 2010]) based on gene function rather than a 1-dimensional line, as in most current genome browsers, based on gene position. This would have the effect of creating a fixed coordinate system in which parts would be associated with function (e.g. cellular membrane, cell cycle, apoptosis, etc), thereby satisfying the Gestalt grouping principle of proximity, which states that elements that are positioned close to one another are perceived as related semantically. This work is in collaboration with BC Cancer Research Centre, TU Wien and City University of London.

Your task will be to define a similarity metric between genes based on their functional similarity, as defined in example the Gene Ontology. Multiple criteria for similarity can be used and possibly tuned to specialize applications, in which some groupings are more relevant than others. Then you will apply clustering and layout algorithms (e.g., spring embedder, stochastic neighborhood embedding) to derive a 2-dimensional layout of the genes. Our expectation would be that genes that are close in this layout are functionally similar. This layout would be reshaped to fit into a rectangular area suitable as a canvas on which data can be drawn, in a similar way as already done for example graphs [Gansner et al., 2010]. Your work will contribute to a paper we plan to submit to the prestigious IEEE InfoVis conference whose papers are published in the highly ranked IEEE Transactions on Visualization and Computer Graphics journal.

Necessary skills:
- Advanced programming experience in Python and Javascript
Preferable but necessary skills:
- Experience using clustering and layout algorithms
- Information visualization design
- Bioinformatics or statistics

Emden R Gansner, Yifan Hu, and Stephen Kobourov. Gmap: Visualizing graphs and clusters as maps. In Visualization Symposium (PacificVis), 2010 IEEE Pacific, pages 201– 208. IEEE, 2010.

12) Field of study: Interactive AI systems

Aalto University unit: School of Electrical Engineering, Department of Communications and Networking

Professor in charge of the topic: Antti Oulasvirta

Academic contact person for further information on topic:  Janin Koch, janin.koch at_12pt.gif aalto.fi

Title of topic: Evaluating interaction behavior in Human-AI collaboration for inspiration

Short task description:                     

Explainability, agency and value alignment are emerging topics regarding the usability and trustworthiness of interactive AI systems. In creative processes this plays an important role due to the underspecification of requirements and objectives.
In a current project we are working on an AI system that interacts with Designers to create inspirational collages (Moodboards) as a part of the design process. During the summer we aim to conduct empirical studies that modify these 3 interaction dimension (Explainability, agency and value alignment) in the context of this interactive collage system.

Your task will be to support planning, conducting and evaluating a qualitative study with Designers that allows us insights into the impact of these interaction dimensions on the quality of the creative process and outcome.

The ideal candidate has experiences in qualitative studies with a background in Interaction design, HCI or similar.

13) Field of study: Mechanical engineering, Rapid prototyping

Aalto University unit: School of Electrical Engineering, Department of Communications and Networking

Professor in charge of the topic: Antti Oulasvirta

Academic contact person for further information on topic:  Sunjun Kim, sunjun.kim at_12pt.gif aalto.fi

Title of topic: The One Button to Simulate All Buttons

Short task description:                     

A feeling of a button is generally expressed by qualitative terms, such as firm, soft, smooth, etc, which is not a scientific way of describing a button. Force-displacement graph is a better expression to describing a button feeling (check https://input.club/the-problem-with-mechanical-switch-reviews/ if you’re interested). Typically the graph is measured from an existing button, but why not in reverse?

During the internship, your task is building a button simulator which is simulating button from a given force-displacement graph. The reference design is the one from Doerrer and Werthschuetzky (https://www.researchgate.net/profile/Roland_Werthschuetzky/publication/228859039_Simulating_push-buttons_using_a_haptic_display_requirements_on_force_resolution_and_force-displacement_curve/links/5472f34f0cf24bc8ea19a8ad.pdf), which consists of a position sensor and a magnetic linear actuator. All the materials are already almost prepared, and you will need to design the structure, build the device, and program it to get it to work.

Image.png

The necessary skills are rapid prototyping (Arduino, 3D CAD), programming (C or Java), basic electric engineering, and basic signal processing skills. Sunjun Kim (sunjun.kim at_12pt.gif aalto.fi) will give you advice and support you. You will also get a chance to participate in a project on dynamic button design for different users and tasks.

14) Field of study: Computer Science, Economy

Aalto University unit: School of Electrical Engineering, Department of Communications and Networking

Professor in charge of the topic: Pekka Nikander

Academic contact person for further information on topic:  Santeri Paavolainen, santeri.paavolainen at_12pt.gif aalto.fi

Title of topic: IoT, Blockchains and Smart Contracts – SOFIE Project

Short task description:                     

SOFIE is an Aalto-led EU H2020 project researching the use of blockchains for IoT device access federation across multiple organizations, multiple blockchains and multiple IoT platforms. It aims to enable cross-organization IoT device operation and through the use of smart contracts enable secure and automated access control to shared and private resources.

SOFIE project has one summer job position open across all the topics listed below. We are welcoming applicants to consider the following topics and their own interest and suitability for them:

  • a) Smart contracts. Development of smart contracts using Ethereum, Iota and/or other suitable distributed ledgers for the purpose of creating proof-of-concept contracts for SOFIE pilot projects. (This requires certain fluency in functional programming.)
  • b) IoT development and testing platform setup. For interoperability and federation development and testing, SOFIE project needs demonstration IoT devices with sensors and actuators to be programmed and set up in multiple different IoT platforms (both cloud and private). (Experience with embedded systems programming, IoT platforms and electronics is helpful for this topic. Knowledge of Linux administration is also useful.)
  • c) Quantifying changes in distributed ledger pricing structure, operation costs etc.The main task is to gather and compose empirical data, statistics and background information on different cryptocurrencies and compose them into short summaries. (It would be helpful for you to have knowledge on at least one of the following areas: valuation statistics, pricing statistics, production value chains especially related to production costs – in case of distributed ledgers electricity and capital costs.)

Please remember to indicate in your application which (one or more) of the topics listed above you are interested in!

15) Field of study: Construction management

Aalto University unit: School of Engineering, Department of Civil Engineering

Professor in charge of the topic: Olli Seppänen

Academic contact person for further information on topic:  Olli Seppänen, olli.seppanen at_12pt.gif aalto.fi

Title of topic: Production planning and control in construction

Short task description:                     

-    Working together as part of an existing research team, helping on the development of a real-time production control system, which aims to provide real-time information of all the construction resources (material, equipment and labor)
-    Supporting case studies in Finland, visiting construction sites, collecting and analyzing data
-    Contributing in research papers

Necessary skills / prerequisites:
-    Good team player (desirable experience working in teams)
-    In Bachelor programme in Civil Engineering, Architecture, Construction Management or similar
-    Good English skills (oral and written)
-    Some experience of construction projects

16) Field of study: Reality Capture

Aalto University unit: School of Engineering, Department of Civil Engineering

Professor in charge of the topic: Olli Seppänen

Academic contact person for further information on topic:  Olli Seppänen, olli.seppanen at_12pt.gif aalto.fi

Title of topic: Reality Capture for Construction Sites

Short task description:                     

-    Working together in an existing team, helping with data collection of images and laser scans on construction sites
-    Supporting case studies in Finland, visiting construction sites, collecting and analyzing data
-    Contributing in research papers

Necessary skills / prerequisites:
-    Good team player (desirable experience working in teams)
-    Demonstrated interest in construction, related field of study is a plus
-    Good English skills (oral and written)
-    Work experience in construction sites is a bonus

17) Field of study: Engineering (Mechanical/ Civil/ others), Computer Science, HCI/ Game Design

Aalto University unit: School of Engineering, Department of Civil Engineering

Professor in charge of the topic: Vishal Singh

Academic contact person for further information on topic: Vishal Singh, vishal.singh at_12pt.gif aalto.fi

Title of topic: Computing and Information Modeling for Design and Construction (Building Information Modeling)

Duration (please note exceptional timing): approx.10 weeks (from first week of May to mid-July)

Short task description:                     

The position is within the Aalto Building Information Modeling (BIM) Collaboration Group in the Civil Engineering Department, under the supervision of Prof. Vishal Singh.
Digitalization and Information and Communication Technology (ICT) in Architecture Engineering Construction- Facilities Management (AEC­FM) industry is a rapidly evolving area that has the potential to shape the next generation of built environment. Generating the digital information and flow of it affects the efficiency of AEC­FM operations and tasks. The research group explores both physical and digital aspects of the future of spaces and the built environment.

Responsibilities

Through the application of relevant research methods, the core research topic will be combining the Internet of Things (IoT), Intelligent/Smart Products, Distributed and Agent­based Systems and Digital Communication areas. The selected candidate will have the possibility to work on the ongoing DigiBuild, VisuaLynk and SpaCyPhy projects which have existing collaboration and links with other research groups and industry partners. The selected candidate will also have the responsibility to contribute to the ongoing platform (web application) development work under the specified projects.

Qualifications

Candidates studying in the field of computing science, automation/ building automation, mechanical engineering, mechatronics and robotics are encouraged to apply. Candidates from core built environment disciplines such as Civil Engineering, Architecture or Construction Technology are also welcome to apply if they have suitable computing and programming experience.
At least intermediate skills in one or more of the following is expected C#, C++, Python, JavaScript, Neo4j, Node js.
Good communication skills are an asset, as well as an open and curious mind in order to integrate well with multidisciplinary teams. Good command of English is a necessary prerequisite.

Period of internship

This internship is planned from first week of May to mid-July (approx. 10 weeks).

18) Field of study: Product Design, Industrial Design, Architecture, Engineering (Mechanical/ Mechatronics/ Civil/ others)

Aalto University unit: School of Engineering, Department of Civil Engineering

Professor in charge of the topic: Vishal Singh

Academic contact person for further information on topic: Vishal Singh, vishal.singh at_12pt.gif aalto.fi

Title of topic: Design and Construction of Cyber Physical Spaces (Design methodology, modeling and prototyping)

Duration (please note exceptional timing): approx.10 weeks (from first week of May to mid-July)

Short task description:                     

The position is within the Aalto Building Information Modeling (BIM) Collaboration Group in the Civil Engineering Department, under the supervision of Prof. Vishal Singh.
Digitalization and Information and Communication Technology (ICT) in Architecture Engineering Construction- Facilities Management (AEC­FM) industry is a rapidly evolving area that has the potential to shape the next generation of built environment. The research group explores both physical and digital aspects of the future of spaces and the built environment.

Responsibilities

Through the application of relevant design research methods, the core research topic will be design related, exploring the future of spaces and built environment when combined with Internet of Things (IoT), intelligent/smart products, distributed and agent­based systems, and digital communication technologies. The selected candidate will have the possibility to work on the SpaCyPhy (Cyber Physical Spaces) and Diction (Intelligent construction) project. This internship will explore the design aspects of such changing cyber physical spaces. The responsibilities include concept generation, detailing, modeling and prototyping. The intern will work with other team members who are working on the ICT and digitalization part of the research.

Qualifications

Candidates studying in the field of product design, architecture, mechanical engineering, mechatronics and robotics are encouraged to apply. Candidates from Civil Engineering are also welcome to apply if they have suitable design and modeling experience.
Candidates need to have good modeling and detailing skills in either BIM software (ArchiCAD, Revit, etc) or CAD applications such as Solidworks, Catia, Creo, Rhino, etc. Experience with 3D prototyping is desirable, including the use of CNC laser cutters, rapid prototyping, etc.
Good communication skills are an asset, as well as an open and curious mind in order to integrate well with multidisciplinary teams. Good command of English is a necessary prerequisite.
This position is open to students both at bachelors and masters level.

Period of internship

This internship is planned from first week of May to mid-July (approx. 10 weeks).

19) Field of study: Electrochemistry

Aalto University unit: School of Chemical Engineering, Department of Chemistry and Materials Science

Professor in charge of the topic: Lasse Murtomäki

Academic contact person for further information on topic: Lasse Murtomäki, lasse.murtomaki at_12pt.gif aalto.fi

Title of topic: Galvani potential driven metal extraction and reduction

Short task description:                     

Extraction and reduction of metals is studied with electrochemical means at the water-oil interface. The process is boosted by the Galvani potential difference across the interface that is created, instead of an external power source, with salts distributing between the phases. Reactions at the interface are followed with the Scanning Electrochemical Microscope (SECM). The target is to find a new means to separate metals from waste solutions, in particular those that cannot be reduced from an aqueous solution due to their high redox potential.

20) Field of study: Wood science, Chemistry, Applied physics

Aalto University unit: School of Chemical Engineering, Department of Bioproducts and Biosystems

Professor in charge of the topic: Lauri Rautkari

Academic contact person for further information on topic:  Lauri Rautkari, lauri.rautkari at_12pt.gif aalto.fi

Title of topic: Novel wood modification techniques

Short task description:                     

In this topic, the student will participate in development of our new wood modification processes.  The student will modify wood samples and characterize the properties. The techniques that he/she will use is depending on the student’s background, the tasks are varying from mechanical properties to cell wall imaging using Raman spectroscopy.

21) Field of study: Materials Engineering, Surface Science and Corrosion

Aalto University unit: School of Chemical Engineering, Department of Chemical and Metallurgical Engineering

Professor in charge of the topic: Mari Lundström

Academic contact person for further information on topic:  Ben Wilson, ben.wilson at_12pt.gif aalto.fi

Title of topic: Corrosion Behaviour of High Entropy Alloys

Short task description:                     

Materials used in current or future energy production often face physical and chemical environments that are extremely hostile. Combinations of chemically corrosive environments, large heat loads, hard ionizing radiation fluxes and, for example, simultaneous mechanical loads, significantly shorten the lifetime of components manufactured from currently available materials

This project will focus on a novel class of single-phase solid solution metal alloys, called high entropy alloys (HEAs) when the number of alloying elements with significant molar fractions is more than five. This multi-metal approach clearly demonstrates that a breakthrough with traditional metallurgy is possible. Beyond that concept, all the possible strategies planned to improve materials and associated microstructures by design approaches are key factors to develop new classes of (functional or structural) materials with enhanced properties. The design challenges of primary interest are corrosion and embrittlement by hydrogen and other mobile impurities into the original material. HEA performance in corrosive environments is a hot topic globally, while the fundamental understanding of these types of materials from the atomic to macroscale is currently missing from the wider scientific literature.

As an AScI Summer Intern in the group of Hydrometallurgy and Corrosion, you would perform innovative experiments in a vibrant research environment and will learn a host of physical/electrochemical techniques required for materials characterisation. The ideal applicant will be highly motivated, willing to learn, have a basic understanding of chemistry and materials - particularly metals and fluency in both written and spoken English. Previous experience in the area of corrosion would be considered an added advantage, but is not obligatory.

22) Field of study: Mineral processing

Aalto University unit: School of Chemical Engineering, Department of Chemical and Metallurgical Engineering

Professor in charge of the topic: Rodrigo Serna

Academic contact person for further information on topic:  Rodrigo Serna, rodrigo.serna at_12pt.gif aalto.fi

Title of topic: Green chemistry for mineral processing

Short task description:                     

The applicant should preferably have background on good practices in a laboratory.

The tasks will consist on the evaluation of new green chemical systems to concentrate valuable minerals, mainly using froth flotation operations.

In addition to the mineral concentration processes, the student will gain knowledge on preparation of the minerals for enrichment, analysis of results and mineralogical characterization.

23) Field of study: Communications Engineering, Signal Processing

Aalto University unit: School of Electrical Engineering, Department of Signal Processing and Acoustics

Professor in charge of the topic: Mikko Vehkapera (with Risto Wichman)

Academic contact person for further information on topic:  Mikko Vehkapera, mikko.vehkapera at_12pt.gif aalto.fi

Title of topic: Energy efficient massive MIMO with low-resolution ADCs

Short task description:                     

The research vision is to develop energy efficient message passing algorithms for joint channel estimation and detection in massive MIMO with low-resolution ADCs at the base station. At first, the student will review relevant research literature to gain understanding on the prospects and challenges of the concept. The research will then progress to solve the emerging signal-processing problems and analyze the energy-performance trade-off of the developed algorithms through theoretical calculations or MATLAB simulations. As for the prerequisites, prospective applicants should have basic understanding on signal processing, linear systems, probability theory and wireless communication theory as well as be capable of surveying research literature and perform scientific writing in English. As per the values of our university, we expect that the appointed student is a creative, self-directed person and capable of critical thinking.

24) Field of study: Machine Learning

Aalto University unit: School of Science, Department of Computer Science

Professor in charge of the topic: Samuel Kaski

Academic contact person for further information on topic:  Hanna Poikela, hanna.poikela at_12pt.gif aalto.fi and Samuel Kaski, samuel.kaski at_12pt.gif aalto.fi

Title of topic: Approximate Bayesian Computation

Short task description:                     

For this project you will join the Aalto Probabilistic Machine Learning Group (http://research.cs.aalto.fi/pml/) to develop probabilistic models and inference techniques. We are developing Approximate Bayesian Computation (ABC) techniques for inference in simulator-based models. The work is basic research in machine learning and inference, with applications chosen to match your interests. We have particularly exciting work on-going in modeling of human behavior and precision medicine. For more information see our ABC software ELFI http://elfi.readthedocs.io and for instance https://arxiv.org/abs/1708.00707 , https://academic.oup.com/sysbio/article/66/1/e66/2420817 . Students with strong background in mathematics and interest in model development are especially encouraged to apply. Skills and interest in programming are a big plus.

25) Field of study: Machine Learning

Aalto University unit: School of Science, Department of Computer Science

Professor in charge of the topic: Samuel Kaski

Academic contact person for further information on topic:  Hanna Poikela, hanna.poikela at_12pt.gif aalto.fi and Samuel Kaski, samuel.kaski at_12pt.gif aalto.fi

Title of topic: Probabilistic machine learning for precision medicine

Short task description:                     

For this project you will join the Aalto Probabilistic Machine Learning Group (http://research.cs.aalto.fi/pml/) to develop probabilistic models and inference techniques. Our core expertise is learning from complex and multiple data sources, a problem domain that arises in a wide range of application fields. During the internship, you will develop non-parametric probabilistic models and Bayesian inference techniques to make personalized predictions for treatment outcomes, taking into account available side information and structure in the data.  Students with strong background in mathematics and interest in model development are especially encouraged to apply.

26) Field of study: Probabilistic machine learning

Aalto University unit: School of Science, Department of Computer Science

Professor in charge of the topic: Aki Vehtari

Academic contact person for further information on topic:  Aki Vehtari, aki.vehtari at_12pt.gif aalto.fi

Title of topic: Bayesian methods for epidemiology, disease risk prediction and personalised medicine

Short task description:                     

The goal is to develop probabilistic modeling, Bayesian inference and machine learning methods for epidemiology, disease risk prediction, and personalised medicine. Ever increasing computing performance makes it possible to use more complex models to model phenomena which are inherently complex containing nonlinearities and interactions. Bayesian approach provides consistent and flexible way to combine available structural information and uncertain observations.  The summer project can be taking part of the methodological development or more applied analysis depending on your interests. Strong background in mathematics and some experience in programming is beneficial.

27) Field of study: Biomedical Engineering and Neuroscience

Aalto University unit: School of Science, Department of Neuroscience and Biomedical Engineering

Professor in charge of the topic: Risto Ilmoniemi

Academic contact person for further information on topic:  Risto Ilmoniemi, risto.ilmoniemi at_12pt.gif aalto.fi

Title of topic: TMS, EMG and EEG

Short task description:                     

The goal is to develop techniques for advanced transcranial magnetic stimulation (TMS). In particular, our aim is to use EEG signals from the brain and EMG signals from muscles to provide feedbck to TMS in real time. The work would involve studying several aspects of how feedback-controlled TMS should and could be implemented.  Depending on the skills and interests of the student, emphasis can be put on experimental work including TMS sequence optimization or on algorithms for artifact rejection or feedback-controlled studies.

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