Available Positions

Positions for summer 2017

Link to positions at (topic numbers in brackets):

School of Science (1-17) - School of Electrical Engineering (18-19) - School of Engineering (20-21)

1) 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: Lasse Laurson, lasse.laurson at_12pt.gif aalto.fi

Title of topic: Micromagnetic simulations of magnetic domain wall dynamics

Short task description:                     

The Aalto CECAM node (www.cecam.org) offers an Aalto CECAM internship in high-performance computational physics.

In this particular position, we investigate numerically domain wall dynamics in various ferromagnetic nanostructures (the specific problem may also be tailored according to the interest and background of the candidate), using a GPU-based micromagnetic simulation code. Possible problems to address include in particular magnetic field and electric current driven domain wall motion in nanowires and strips, which are also relevant for emerging domain wall based logic and memory devices. The successful candidate is expected to possess basic skills in programming and data analysis, including familiarity with the standard Linux environment, and an undergraduate level understanding of basics of magnetism.

2) 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: Antti Puisto, antti.puisto at_12pt.gif aalto.fi

Title of topic: Modeling shear flow and thixotropy of confined low density particulate gels

Short task description:                     

The Aalto CECAM node (www.cecam.org) offers an Aalto CECAM internship in high-performance computational physics.

Here, we study the evolution of structures formed of long range attractive soft particles using
discrete element based methods. The particles, confined in a Hele-Shaw shell, interact via long
range van der Waals potential forming clusters. We analyze the evolution of the clusters under imposed shear flow to determine their mass to volume fractal dimension and lacunarity to learn the influence of shear to the geometry of the clusters. Furthermore, stagnated flow is studied to observe the structure changes due to structure relaxation. The applicant should have experience in programming and have basic knowledge in soft matter physics.

3) 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

Academic contact person for further information on topic: Martha Arbayani Zaidan, martha.a.zaidan 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 datasets. The more data fed into an ML system, the more it can learn and apply the results to higher quality insights.

In this project, we will investigate and implement ML methods for finding key variables influencing physical phenomena and modeling them. The examples include investigating key variables in aerosol formation in the atmosphere and modeling its physical processes using ML strategies.

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

4) Field of study: Physics, Chemical engineering, Physical chemistry, New energy technologies

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

Professor in charge of the topic: Peter Lund

Academic contact person for further information on topic: Dr Muhammad Imran Asghar, imran.asghar at_12pt.gif aalto.fi

Title of topic: Synthesis of core-shell nanostructured materials for improved fuel cell performance

Short task description:                     

New type of ionic conductors could speed up the development of fuel cells, which are efficient clean energy conversion devices. In particular, nanocomposites utilizing robust core-shell nanostructured materials for efficient ion transport and reaction kinetics are of great interest. The electrochemical and microstructural properties of these materials depend on their composition and the synthesis method. In this internship, the student will help in synthesizing these nanomaterials using different procedures including solid-state reaction route, freeze-drying route, co-precipitation method, for improved fuel cell performance. The synthesized materials will be characterized with various microscopic and spectroscopic tools.  The applicant is required to have basic understanding of chemistry and chemical handling. Any prior experience of working in the chemistry laboratory is an advantage, however, it is not mandatory. The applicant must be a motivated person to learn new techniques. The applicant should have good English language skills.

5) Field of study: Applied Physics or Electrical Engineering and Automation

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

Professor in charge of the topic: Robin Ras, Instructor: Dr Bo Chang

Academic contact person for further information on topic: Robin Ras, firstname.lastname [at] aalto [dot] fi

Title of topic: Deposition of nanoliter droplets using hydrophilic/superhydrophobic patterned surfaces

Short task description:                     

Recently, surfaces with hydrophilic pads surrounded by superhydrophobic substrate have attracted great interests in many applications including capillary self-alignment (Chang et al, Sci. Rep. 2015. http://www.nature.com/articles/srep14966) and liquid deposition (Chang et al, APL 2016. http://dx.doi.org/10.1063/1.4947008). In our research group, we have developed a facile gravity-induced sliding droplets method for deposition of nanoliter sized droplets using hydrophilic/superhydrophobic patterned surfaces. The deposition process is parallel where multiple different liquids can be deposited simultaneously as shown in Fig.1.

Bo_Chang_1_cropped.png

Fig.1. Sketch and illustration of deposition by sliding droplets on patterned hydrophilic/superhydrophobic surface: (a) different kinds of liquids are placed on the first row of a 10x10 matrix of hydrophilic pads surrounded by superhydrophobic substrate; (b) zoomed view of sliding droplets deposition process; (c) side view of a droplet sliding on a patterned surface; (d) zoomed view of the rear edge of the contact line pinned on the hydrophilic pad.

As an AScI Summer Intern in our group, you would carry out cutting edge experiments to study the dynamics of the droplets on hydrophilic/superhydrophobic patterned surfaces. You would also learn modeling techniques for studying droplets. The work will be done in the SMW group, which is part of the HYBER Center of Excellence from the Academy of Finland (http://hyber.aalto.fi/ ).

6) Field of study: Applied Physics or Electrical Engineering and Automation

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

Professor in charge of the topic: Robin Ras, Instructor: Dr Bo Chang

Academic contact person for further information on topic: Robin Ras, firstname.lastname [at] aalto [dot] fi

Title of topic: Fabrication of soft and elastic hydrophilic/superhydrophobic patterned surfaces

Short task description:                     

Soft micro devices and stretchable electronics have attracted great interest for their potential applications in sensory skins and wearable bio-integrated devices. One of the most important steps in building printed circuits is the alignment of assembled micro objects. Previously, capillary self-alignment of microchips driven by surface tension effects has been demonstrated to be able to achieve high-throughput and high-precision in integration of micro parts on rigid hydrophilic/superhydrophobic patterned surface (Chang et al, Sci. Rep. 2015. http://www.nature.com/articles/srep14966). In this project, we study the fabrication of soft and elastic hydrophilic/superhydrophobic patterned surfaces and capillary self-alignment of microchips on such patterned soft and elastic template.

In our research group, we have developed a simple process for making hydrophilic/superhydrophobic soft surfaces by replicating patterned black silicon surfaces onto soft PDMS surface. As an AScI Summer Intern in our group, you would perform cutting-edge experiments in a dynamic research environment. You would learn how to prepare soft patterned hydrophilic/superhydrophobic surfaces and study the capillary self-alignment process on the soft patterned surfaces. The work will be done in the SMW group, which is part of the HYBER Center of Excellence from the Academy of Finland (http://hyber.aalto.fi/).

7) Field of study: Soft Matter Physics

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

Professor in charge of the topic: Jaakko Timonen

Academic contact person for further information on topic: Prof. Jaakko Timonen, jaakko.timonen at_12pt.gif aalto.fi

Title of topic: Magnetic trapping of active microswimmers

Short task description:                     

Development of new techniques for manipulation (trapping, translation, etc.) of individual living micro-organisms is important for many fields - ranging from biophysics to applied medical technologies. In this project, the student will design and fabricate new magnetic tweezers and study trapping of active microswimmers (such as Chlamydomonas reinhardtii).

Prerequisites: Studies in soft matter physics, fluency in English

More information: http://physics.aalto.fi/en/groups/active/research/

8) Field of study: Computer science

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

Professor in charge of the topic: Mario Di Francesco

Academic contact person for further information on topic: Mario Di Francesco, mario.di.francesco at_12pt.gif aalto.fi

Title of topic: Modeling the Internet of Things

Short task description:                     

The Internet is going to be very different from what it is now. As a massive number of embedded devices and smart objects are becoming connected, the Internet is turning from a network of people and their applications to a planet-scale interconnection of machines. Indeed, the rise of the Internet of Things (IoT) brings a radical change in our global communication network and its consequences are not yet understood.

The purpose of this summer internship is to model the IoT with the ultimate goal to understand its properties. How to design network architectures for optimal performance of IoT applications? What are the fundamental performance limits of the IoT? What can be achieved by leveraging computing capabilities of billion devices?

Successful applicants should be proficient in at least one of the following thematic areas: graph theory, network optimization, stochastic modeling, or probability theory. Familiarity with the IoT is considered as a plus.

9) Field of study: Computer Science

Aalto University unit: School of ScienceDepartment of Computer Science

Professor in charge of the topic: Alex Jung

Academic contact person for further information on topic:  Alex Jung, alexander.jung at_12pt.gif aalto.fi

Title of topic: Compressed Sensing over Complex Networks for Learning from Big Data

Short task description:                     

The student is supposed to try out different combinations of tools from network science (e.g., clustering algorithms) and compressed sensing (e.g., convex optimization methods) for designing efficient learning algorithms for massive distributed datasets.

Necessary Skills: Basics from Linear Algebra/Convex Optimization/Probability Theory

10) Field of study: Computer Science

Aalto University unit: School of ScienceDepartment of Computer Science

Professor in charge of the topic: Alex Jung

Academic contact person for further information on topic:  Alex Jung, alexander.jung at_12pt.gif aalto.fi

Title of topic: Sparse Label Propagation for Semi-Supervised Learning from Big Data over Networks

Short task description:                     

The student is supposed to investigate generalizations of the popular label propagation algorithm for semi-supervised learning. In particular, these generalizations aim at exploiting the tendency of real-world networks to form communities, i.e., to form clusters. 

Necessary Skills: Basics from Linear Algebra/Convex Optimization/Probability Theory

11) Field of study: Computer Science

Aalto University unit: School of ScienceDepartment of Computer Science

Professor in charge of the topic: Alex Jung

Academic contact person for further information on topic:  Alex Jung, alexander.jung at_12pt.gif aalto.fi

Title of topic: Learning Networks for Big Data

Short task description:                     

One of the most powerful approaches to cope with big data is via representing the massive datasets as networks or graphs. Indeed, social networks or gene regulatory networks induce a natural network structure to the observed data. Moreover, the most successful machine learning methods (deep neural
networks) are based on networks. In many applications the network has to be learned from training data. In this project, students should develop novel methods, mainly based on modern convex optimization algorithms, for learning the network structure inherent to massive amounts of data.

Necessary Skills: Basics from Linear Algebra/Convex Optimization/Probability Theory

12) Field of study: Computer Science

Aalto University unit: School of ScienceDepartment of Computer Science

Professor in charge of the topic: Alex Jung

Academic contact person for further information on topic:  Alex Jung, alexander.jung at_12pt.gif aalto.fi

Title of topic: Deep Learning via Compressed Sensing over Complex Networks

Short task description:                     

Deep learning via deep neural networks is currently considered as the most powerful generic approach to artificial intelligence. In this project, students will  explore fundamentally new approaches to learning via deep neural networks by drawing on our recent work on compressed sensing over complex networks. The precise topic will be adapted to the student's interest and background.

Necessary Skills: Basics from Linear Algebra/Convex Optimization/Probability Theory

13) Field of study: Computer Science

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

Professor in charge of the topic: Juho Kannala

Academic contact person for further information on topic: Juho Kannala, first.last [at] aalto [dot] fi

Title of topic: Computer Vision

Website of the research group: http://users.aalto.fi/~kannalj1/

Short task description:                     

We are a relatively new research group working broadly in the field of computer vision. We are pursuing research problems both in geometric computer vision (including topics such as visual SLAM, visual-inertial odometry, and 3D scene reconstruction) and in semantic computer vision (including topics such as object detection and recognition, and deep learning). We are looking for students interested in both basic research and applications of computer vision. Students with good programming skills and strong background in mathematics are especially encouraged to apply. Previous experience in computer vision is not required. The precise topics of the research will be chosen together with the students to match their personal interests. For more information about our research, please visit http://users.aalto.fi/~kannalj1/

14) 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: Samuel Kaski, first.last [at] aalto [dot] 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 (https://arxiv.org/abs/1612.00653) and precision medicine. We have recently released ABC software: https://github.com/hiit/elfi . 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.

15) 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: Samuel Kaski, first.last [at] aalto [dot] 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.

16) 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, first.last [at] aalto [dot] 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.

17) Field of study: Industrial Development and Management

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

Professor in charge of the topic: Ilkka Kauranen

Academic contact person for further information on topic: Timo Nyberg, timo.nyberg at_12pt.gif aalto.fi

Title of topic: Intellectual property (IP) in social media and distributed production

Short task description:                     

We are looking for an open-minded person who has great interest in:
a) Modern social media platforms e.g. FaceBook, Kakao, WeChat, WhatsApp
b) Distributed/additive manufacturing e.g. 3D-printing and co-engaging production and
c) Intellectual property rights e.g. patents, copyrights, designs, and trademarks.
In at least one of the three areas you need to have deep understanding and practical skills.

The task will be with a group of international researchers from diverse disciplines (engineering, law, psychology, management). We are studying the future production models and how, by whom, when, and where the value of creative work i.e. intellectual property is generated and how the value can be captured? Related areas of research are circular economy, platform economy, social manufacturing, and co-engaging production.

There is a new trend that we call co-engaging production. Thanks to the new information and communication technologies (ICT) more products and services can be produced in small scale and/or distributed facilities close to the customers or in specialized networks (e.g. micro breweries, Uber, AirBnB, 3D-print shops). In the social networks the demand and offering can meet easily at low transaction cost. However, one big question is how the intellectual property rights can be managed in the new operating models. Do we need new types of intellectual property that fit better small and distributed production?  

If you have skills in one or more of the three areas: social media, 3D production technologies, or intellectual property, please, don’t hesitate to send in your application today. We are looking for a great time together with you.

18) 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: Taneli Riihonen (with Risto Wichman)

Academic contact person for further information on topic: Taneli Riihonen, firstname.lastname [at] aalto [dot] fi

Title of topic: The fusion of automotive radars and millimeter-wave wireless communications

Short task description:                     

The research vision is to develop advanced signal processing techniques that allow using super/extremely high frequency (SHF/EHF) bands for simultaneous or co-existing radar and wireless data transmission. A promising application scenario for this technology could be vehicular communications between cars and roadside access points. 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 performance trade-offs in co-existing radar and wireless transmission through theoretical calculations or MATLAB simulations. As for the prerequisites, prospective applicants should have basic understanding on signal processing, linear systems 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.

19) Field of study: Robotics and Automation, Mechatronics Engineering, Electrical Engineering

Aalto University unit: School of Electrical Engineering, Department of Electrical Engineering and Automation

Professor in charge of the topic: Quan Zhou

Academic contact person for further information on topic: Prof. Quan Zhou, quan.zhou at_12pt.gif aalto.fi

Title of topic: 3D printing of heterogeneous structures

Short task description:                     

Recent development in 3D printing has opened a new path for printing heterogeneous structures, including new entirely soft robot by combining soft lithography, molding and 3D printing. In our research group, we have developed a customized 3D printing system for printing different materials and structures.

As an AScI summer intern in our group, you would learn to use the system to print different materials and program the system to print different structures. You would also optimize the printing parameters for each material. The work will be done in the Micro- and nanorobotics group, which is actively working on micro- and nanorobotic manipulation and automation methods, including acoustic manipulation, microassembly, magnetic micromanipulation, nanoforce characterization, autonomous micromanipulation, and their applications in biomedical, material and industrial applications. (http://eea.aalto.fi/en/research/micronanorobotics/  ).

20) Field of study: Building Information Modeling

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: Aalto Building Information Modeling (BIM) Collaboration Group internship

Duration (please note exceptional timing): 9-10 weeks (1st week of May- 1st/2nd week of July)

Number of positions: 2

Short task description:                     

The summer internship position is within the Aalto Building Information Modeling (BIM) Collaboration Group (URL: http://bim.aalto.fi/ ) in the Department of Civil Engineering, under the supervision of Assistant Professor Vishal Singh. Information and Communication Technology (ICT) in Architecture-Engineering- Construction and Facilities Management (AEC-FM) industry is a rapidly evolving area that has the potential to shape the next generation of built environment. This internship gives candidates the opportunity to work on such transformational projects.

Responsibilities

The selected candidates will have the possibility to work on one of the ongoing projects such as DigiBuild (BIM and Internet of Things for operations and maintenance of buildings), VisuaLynk (BIM,
Internet of Things, and data aggregation), InBookMode (Interactive book and model environment,
Learning platform, gaming and augmented reality) and SpaCyPhy (Cyber Physical Spaces, space as
a service). During the internship period the selected candidates will work in a team with other
doctoral and master thesis students in the group. Most of these projects require multidisciplinary skill sets combining expertise associated with design thinking, engineering design, computational thinking, Internet of Things (IoT), systems engineering, software applications and digital communication.

Qualifications

Candidates in 3rd year of bachelor’s studies or in 1st year of master’s studies are preferred. Candidates from the field of computing science, automation/building automation, mechatronics and robotics are encouraged to apply. Good programming skills in one or more of the following is a plus: c#, python, javascript. Candidates with design background and from core built environment disciplines such as Civil Engineering, Architecture or Construction Technology are also welcome to apply.

Other activities

The selected candidates will not only be working as a summer intern for the stipulated period, but they will also be expected to join a one-week Aalto BIM Summer School that will expose them to various aspects of BIM and related digital developments in construction sector. The one-week summer school (3 credits) will be organized from 22nd-29th of May, which means it is included within the 10 week internship period.

21) Field of study: Transportation Systems

Aalto University unit: School of Engineering, Department of Built Environment

Professor in charge of the topic: Milos Mladenovic

Academic contact person for further information on topic: Milos Mladenovic, first.last [at] aalto [dot] fi

Title of topic: Advanced analysis of urban public transport networks

Website of the research group: http://builtenv.aalto.fi/en/research/spatial_planning_and_transportation_engineering/transportation/

Short task description:                     

The reserch will be part of the project DecoNet: Decoding Urban Public Transport Networks, funded by the Academy of Finland. This joint project of the Department of Computer Science and Department of Built Environment of Aalto University is a collaboration between the fields of computer science and transport engineering. The task will involve supporting collection and curation of open data on public transport networks from a wide range of cities, to be included as a part of an open access repository. In addition, collected data will be analyzed using network-theoretical methods to uncover common features, focusing in particular on system resilence and robustness. To study the resilience and robustness of public transport networks, task will investigate more and/or less abrupt disruptions, such as shutdowns of lines, vehicles, and stops. The analysis will center on simulation framework, taking into account alternatives to optimal routes with the help of the randomized shortest path framework already developed in the project. This acquired understanding will be packaged as a set of decision-support tools for improving public transport planning and operation.

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