HICT Spring call projects

HICT Spring call 2018 projects for doctoral students

There are two ways to participate in the present HICT Spring call 2018. If you wish to be considered as a potential new doctoral student in HICT you can (1) apply generally for a PhD position in the research group of any HICT supervisor(s) of your choice. All group descriptions can be found through the list at “www.hict.fi/supervisors”. You can also (2) apply directly to one or a number of specific PhD projects / positions, listed here below.

How to apply? Read more information at https://www.hict.fi/spring_2018

 

Algorithms and Machine Learning (AML)

Project 1:

Deep and Shallow Learning for Extreme Multi-label Classification

Supervisor: Prof. Rohit Babbar (Aalto University, Department of Computer Science)

Extreme multi-label classification refers to supervised multi-label classification with hundreds of thousand or even millions of labels [1]. This framework has applications in various areas of machine learning such as recommendation systems, automatic labeling, ranking and web-advertising. The goal of the proposed PhD thesis would be to advance the state-of-art in this domain by building novel, scalable and efficient, deep and shallow learning algorithms while leveraging existing expertise in this domain [2]. With a significant number of open datasets and source codes [1], the thesis promises to be an exciting research topic enabling possibilities for algorithmic, theoretical and empirical analysis.

[1] Extreme Classification Repository - http://manikvarma.org/downloads/XC/XMLRepository.html

[2] DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification, https://arxiv.org/abs/1609.02521

 

Project 2:

Open doctoral student position(s) in satisfiability, optimization, knowledge representation and reasoning, constraint solving and machine learning

Supervisor: Prof. Matti Järvisalo (University of Helsinki, Department of Computer Science)

We invite applications from students interested in PhD research around one or more of the following research themes: automated reasoning techniques and solvers, declarative optimization, computational complexity issues in knowledge representation and reasoning,  interconnections between constraint solving and machine learning.

Research group: Constraint Reasoning and Optimization, https://www.hiit.fi/cosco/coreo/

 

Project 3:

Doctoral student positions in computer vision and machine learning

Supervisor: Prof. Juho Kannala (Aalto University, Department of Computer Science)

Abstract:: Computer vision is a rapidly developing field that is at the forefront of recent advances in artificial intelligence. Our group has broad research interests within computer vision. We are pursuing problems both in geometric computer vision (including topics such as visual SLAM, visual-inertial odometry, image-based 3D modeling and localization) 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. The precise topics of the research will be chosen together with the students to match their personal interests. Examples of our recent papers include arxiv.org/abs/1708.00894 , arxiv.org/abs/1707.09733 and arxiv.org/abs/1705.03386. For more papers and further information visit: https://users.aalto.fi/~kannalj1/

 

Project 4:

Probabilistic machine learning

Supervisor: Prof. Samuel Kaski (Aalto University, Department of Computer Science)

I am looking for a student eager to join the Aalto Probabilistic Machine Learning Group to develop new probabilistic models and inference techniques. Particularly promising thesis topics at the moment are (1) Approximate Bayesian Computation (ABC) techniques for inference in simulator-based models, and (2) flexible models (read: deep Bayesian learning and Bayesian deep learning). We have also excellent opportunities for applying the techniques across a range of practical applications, for instance in interactive machine learning and personalized medicine. Requirements: background in math, stats or cs and eagerness to learn the rest.

 

Project 5:

3 open doctoral student positions in Data Science and Analytics

Supervisor: One of multiple HICT professors working on Data Science in Aalto University; Call coordinated by Prof. Samuel Kaski

In the area of ICT, Aalto is one of the leading European universities, with Data Science as one of its main strengths. Aalto has joined forces with University of Helsinki, the other strong university in Finland, to form an excellent research environment linked with a range of application domains in science and society. Aalto offers 3 PhD positions in Data Science, developing and applying data science methods. The methods include machine learning, data mining and probabilistic modelling (Bayesian methods, neural networks and deep learning, graphical models, kernel methods, statistical inference). The applications are in bioinformatics, digital health, business and financial analytics, complex systems, human-computer interaction, information retrieval and visualization, neuroinformatics, and signal processing. Please indicate your preferences on topics and supervising professors in the cover letter.

 

Project 6:

Open doctoral student position in machine learning

Supervisor: Prof. Arto Klami (University of Helsinki, Department of Computer Science)

The research group of Assistant Professor Klami (https://www.hiit.fi/cosco/mupi) studies statistical machine learning, developing computationally efficient and easy-to-use methods for solving complex artificial intelligence and data science challenges. We are looking for outstanding PhD students to join the group, to work in the intersection of machine learning methods development (Bayesian methods, probabilistic programming and deep learning) and interesting applications ranging from physics to games and economy. Candidates with background in computational physics, applied mathematics and other computational fields are also encouraged to apply.

 

Project 7:

Open doctoral student position in Prof. Kai Puolamäki’s group

Supervisor: Prof. Kai Puolamäki (Aalto University, Department of Computer Science)

Currently there is a significant amount of work on developing algorithms to find complex patterns and relations from data. The learning algorithms such as deep learning methods are often “black boxes”, which means that it is quite difficult for a user to understand how they actually work and produce the results. This applies to most of the state-of-the-art supervised and unsupervised learning methods. Improving human understanding of how these algorithms work will decrease the inherent risk associated with the interpretation and utilisation of the result and also improves the algorithms' robustness and failure tolerance.

The tasks include, e.g., developing methods for explorative data analysis, applying statistical randomisation methods in a novel way to practical problems in data usage as well as studying how state-of-the-art algorithms (classifiers, deep learning networks etc.) can be made more transparent. The position is available in the group of Prof. Kai Puolamäki (http://kaip.iki.fi/group.html).

 

Project 8:

Reliable Automated Bayesian Machine Learning (RAB-ML)

Supervisor: Prof. Aki Vehtari (Aalto University, Department of Computer Science)

We will develop theory and methods for assessing the quality of distributional approximations based on leave-one-out cross-validation and projection predictive model reductions, and seek to improve the inference accuracy by targeting the approximation towards the eventual application goal and by better utilising the available data, e.g., when having data with privacy constraints. To guarantee wide applicability of the project results in data science industry and academic research, the novel methods will be evaluated on a range of practical machine learning models and implemented as part of the leading open-source probabilistic programming systems.

 

Project 9:

Open doctoral student position: "Construction and Classification of Discrete Mathematical Structures"

Supervisor: Prof. Patric Östergård (Aalto University, Department of Communications and Networking)

There is an open position for a student with a background in computer science or discrete mathematics who is interested in developing state-of-the-art algorithms for constructing and enumerating combinatorial structures (codes, designs, graphs, matrices). A 500-core compute cluster owned by the team is a dream come true for anyone wanting to push the limits of what is computationally doable. CERN hunts new particles, we hunt new mathematical structures.

 

Project 22:

PhD student position on algorithms for content analysis and distribution in social media

Supervisor: Prof. Aristides Gionis

We are looking for highly-qualified and motivated doctoral students to work on algorithms for content analysis and distribution in social media. Topics of interest include analysis of social-media content, controversy and polarization in social networks, echo chambers, dissemination of news in social media, opinion-formation models, and algorithms for online content recommendation. The PhD position is in the Data Mining group of Aalto University. Successful applicants are expected to have completed successfully a Masters degree from a reputable international university, and have familiarity with graph mining, machine learning, and/or combinatorial optimization.

Data Mining group website: http://research.cs.aalto.fi/dmg/

 

Life Science Informatics (LSI)

Project 10:

Doctoral candidate position: Algorithmic Designs for Biomolecular Nanostructures (ALBION)

Funding source: Academy of Finland (2017-2021).

Supervisor: Prof. Pekka Orponen (Aalto University, Department of Computer Science)

The project ALBION develops effective algorithmic methods and novel combinatorial models for the rapidly emerging new area of DNA nanotechnology (https://en.wikipedia.org/wiki/DNA_nanotechnology). A current challenge in this direction is for example extending our recent work on DNA renderings of 3D wireframe designs (http://sci.aalto.fi/en/current/news/2015-07-23/) into RNA, with the goal of eventually transcribing and folding such designer molecules in a cellular environment. This work is pursued in collaboration with leading European laboratory partners. For further information, please see the research group page at http://research.cs.aalto.fi/nc/).

 

Project 11:

Foundations of Computational Health (FCHealth)

Funding source: HIIT FCHealth programme

Contact person: Prof. Juho Rousu (juho.rousu@aalto.fi) (several supervisors, see below)

Foundations of Computational Health Research programme of Helsinki Institute for Information Technology HIIT is looking for PhD students to work on cutting-edge technologies for Computational Health. The positions allow combining state-of-the-art computational methods with large-real world data arising in healthcare and personalized medicine, analysed in collaboration with experts from Aalto University, University of Helsinki, Hospital District of Helsinki and Uusimaa (HUS) as well as Institute for Molecular Medicine Finland (FIMM). Specific topics include: Analysis of high-throughput omics datasets (Prof. Tero Aittokallio), Complex networks modelling and mining (Prof. Aris Gionis, Prof. Jari Saramäki), Computational metabolomics  (Rousu), Pan-genome algorithmics (Prof. Veli Mäkinen, Prof. Keijo Heljanko), Machine learning on structured big data (Rousu, Gionis, Aittokallio) Modelling drug resistance (Prof. Ville Mustonen, Aittokallio), Network pharmacology modelling (Aittokallio, Rousu).

 

Project 23:

Intelligent Crop Production

Supervisors: Prof. Samuel Kaski and Prof. Hiroshi Mamitsuka

Due to rising population, global crop production needs to double by 2050. Now boosting crop yields is important, for which we are thinking the key technique will be artificial intelligence. This project is looking for a junior researcher, who can build up a data-integrative machine learning framework to be further integrated with crop growth simulation. This project has not been conducted yet in any place in the globe and might change the world of crop breeding.
 

 

Networks, Networked Systems and Services (NNSS)

Project 12:

PhD student position on "Foundations of Distributed Computing for Big Data"

Supervisor: Prof. Keijo Heljanko (Aalto University, Department of Computer Science)

The PhD student will work in the research group of Assoc. Prof. Keijo Heljanko with focus on Distributed Computing. The group works both on applied algorithms development research on Big Data processing and large scale Distributed Database platforms, as well as on the theoretical foundations of these systems, including theory of concurrent and distributed systems. Typical application areas of the group are the massively parallel processing of next generation sequencing data in Genomics, and the processing of Big Data from the process industry. The profile of the candidates should include either a strong practical background on parallel and distributed computing (e.g., Apache Spark, Hadoop) or a strong theoretical background in algorithms design or concurrency theory. In either case, the PhD student position will be on practical systems work combined with work on the theoretical foundations of large scale distributed systems, such as Big Data platforms and large scale Distributed Databases.

 

Project 13:

SmartCom: Communication and validation of smart data in IoT-networks

Supervisors: Prof. Pekka Nikander, Jari Juhanko, Petri Kuosmanen (Aalto University, Department of Communications and Networking)

The EU metrology project SmartCom is looking for a multi-disciplinary Ph.D. student to work with making metrological measurement instruments into Internet-connected things.  The goal of the project is to develop, provide and distribute a well-engineered and formal framework for the transmission of metrology data over the Internet. The actual work will start from June 2018, at the same time with the project.  The application shall have a M.Sc. degree in metrology, computer science, or telecommunications, and shall have an excellent understanding of IoT systems.

 

Project 14:

SOFIE: Secure Open Federation of Internet Everywhere

Supervisors: Prof. Pekka Nikander, Timo Seppälä (Aalto University, Department of Communications and Networking)

The EU project SOFIE is looking for a highly motivated PhD student to work within the next generation of digital value chains and so-called 4th generation business platforms, to understand the role of incentives in the marketplace as means and causes to increase participation, value generation, and market growth.  The 4th generation business platforms are characterised by the use of distributed ledger technology (DLT), or so-called blockchains, federating across multiple organisations, and the use of smart contracts.  The applicant should have a master's degree (or equivalent) in economics (economic modelling, macro economics, or similar) and shall be knowledgeable in the areas of computer security, cryptography, and distributed systems, or alternatively have a master's degree in computer security and strong understanding of economic modelling.

 

Software and Service Engineering and Systems (SSES)

Project 15:

Efficient algorithms on multi-model databases

Supervisor: Prof. Jiaheng Lu (Department of Computer Science, University of Helsinki)

Abstract: Databases play an important role in our world today. But one of the greatest challenges in current database systems is the "Variety" of the data. Multi-model databases have emerged to address this challenge by supporting multiple data models against a single, integrated backend. Unfortunately, the existing principles and research ideas of multi-model data management are so scarce and far from perfect. This project will tackle this challenge from the fundamental problems to practical techniques. We will propose novel solutions for pressing problems on multi-model data management, including unified data storage abstraction, unified querying and indexing techniques, relaxing consistency model and fine-grained multi-model isolation. As long term contribution, this project supports to build next-generation platform to solve the "Variety" challenge of big data.

 

Project 16:

Open doctoral student position in the TwinRotor project (AoF)

Supervisor: Prof. Martti Mäntylä (Aalto University, Department of Computer Science)

Abstract: The TwinRotor project aims to improve the behaviour of rotating machinery using a digital twin coupled with Industrial Internet methods to support enhanced data flow between the machinery, simulation based virtual sensors, and applied big data analytics. The ambition is to gain insights into the improvement of the rotating machinery design, and to better operational efficiency of the machinery and to an enhanced quality of the products manufactured with the machinery. The wider scientific objective is to study how Industrial Internet methodologies coupled with machine learning can be applied especially to complex engineering design. The role of the person sought focuses on the digital twin architecture and especially the application of machine learning / data analysis methods in the project.

 

User Centered and Creative Technologies (UCCT)

Project 17:

Computer graphics+computer vision+machine learning=profit

Supervisor: Prof. Jaakko Lehtinen (Aalto University, Department of Computer Science)

Abstract: Join a world-leading group in enabling entirely novel capabilities for machines to understand the world through visual and auditory inputs by combining machine learning with computer graphics models, e.g., physically-based image synthesis techniques. Prof. Lehtinen’s group is the origin of several world-first algorithms in image synthesis (1), one-shot material appearance capture (2), performance capture (3), as well as photorealistic generative modelling of human faces by learning from examples (4; google “nvidia gan celebrity” for press). The combination of these directions holds great promise for incredibly exciting results in the near future.

(1) https://mediatech.aalto.fi/publications/graphics/GMLT/

(2) https://mediatech.aalto.fi/publications/graphics/NeuralSVBRDF/

(3) http://research.nvidia.com/publication/2017-07_Audio-Driven-Facial-Animation

(4) http://research.nvidia.com/publication/2017-10_Progressive-Growing-of

 

Project 18:

Open doctoral student position in Tuukka Ruotsalo’s group:  Neuro-adaptive Intention Learning

Supervisor: Doc. Tuukka Ruotsalo (University of Helsinki, Department of Computer Science) 

The group is looking for a PhD student interested in applications of machine learning, in particular deep learning, in analyzing human brain-signals for information retrieval and generation. Background and interest in data science, machine learning, modeling or computational neurosciences is required.

More information: http://augmentedresearch.hiit.fi/

 

Project 19:

There are two projects available for a potential doctoral candidates, please see the details below. In case you are interested in applying to either to project A or project B (or both), please select "Project 19" in the online application form.

Project A. New­s­Eye — A Di­gital In­vest­ig­ator for His­tor­ical News­pa­pers

Supervisor(s): Prof. Hannu Toivonen, Mark Granroth-Wilding (University of Helsinki, Department of Computer Science)

We are looking for a PhD student to join our new European project where we will develop novel methods and tools for digital historians. An ideal candidate will have a strong background in computer science or language technology, with interests in topics such as natural language processing, topic models, text mining; big (text) data, data science; natural language generation, computational creativity.

Newspapers collect information about cultural, political and social events in a more detailed way than any other public record, and in the last decades, tens of millions of newspaper pages from European libraries have already been digitized. The international, multi-disciplinary NewsEye project will develop methods and tools for effective exploration and exploitation of this rich resource by means of new technologies and “big data” approaches. Join the Discovery research group to work on one of the following topics:

1. develop methods for contextualized and contrastive content analysis that is carried out dynamically, per request;

2. develop methods and tools for automated, iterative analysis of corpus content and reporting of the results, culminating in a Personal Research Assistant that functions as the user’s intelligent and transparent aid

Link to the group web pages: http://www.helsinki.fi/discovery

Project B. Digital Language Typology

Supervisor(s): Hannu Toivonen, Mark Granroth-Wilding

We are looking for a PhD to join an ongoing Academy of Finland project, Digitial Language Typology. The candidate should have a strong background in Computer Science or Language Technology and have an interest in working in natural language processing and machine learning. They should have at least some basic knowledge of machine learning.ce; natural language generation, computational creativity. 

The project aims to perform linguistic typology -- mapping different types of connections between languages -- by automatically discovering relevant information from linguistic data. Instead of performing detailed linguistic studies of the languages, we are investigating how much we can discover computationally from text or speech data. We are collaborating with researchers in phonetics to analyse speech data. Our work so far has focussed on unsupervised learning of correspondences between symbols in different languages. There is a broad scope of PhD projects that could be pursued within the project.

 

Project 20:

Open doctoral student position in formal methods, computer-aided system design, learning in system design, cyber-physical systems

Supervisor: Prof. Stavros Tripakis (Aalto University, Department of Computer Science)

Applications are invited from students interested in research around the following themes: formal methods, computer-aided system design, learning in system design, cyber-physical systems.

 

Project 21:

Open doctoral student position in Prof. Antti Oulasvirta’s group – computational methods in HCI

Supervisor: Prof. Antti Oulasvirta (Aalto University, Department of Communications and Networking)

“This ERC funded group is looking for PhD students interested in applications of computational methods in HCI. Background and interest in data science, machine learning, modeling, neurosciences, or cognitive science is required. PhD topic will be negotiable. On-going topics in 2018 include, but are not limited to: 1) Interaction techniques for collaborating with an artificial intelligent agent in complex scientific tasks; 2) modeling of input using control theory, neuromechanics; 3) reinforcement learning models of human-computer interaction; 4) computational models of emotion.”

http://userinterfaces.aalto.fi/