Postdoctoral researcher positions in information and computer science (DL 2 April 2012)

[This document in pdf]

The Department of Information and Computer Science at Aalto University in Espoo/Helsinki, Finland, pursues research on advanced computational methods for modelling, analysing, and solving complex tasks in technology and science. The research aims at the development of fundamental computer science methods for the analysis of large and high-dimensional data sets, and for the modelling and design of complex software, networking and other computational systems.

To promote its ambitious research agenda, the Department is seeking postdoctoral researchers. While the present call focuses on the topics listed below, outstanding candidates in other areas of information and computer science compatible with the Department’s mission are also welcome. Applications should be received at latest on 2 April 2012 for full consideration. The Department may decide to make offers to exceptional candidates already before the end of the call.

Topics

  1. Distributed computing (contact: Prof Keijo Heljanko, keijo.heljanko@aalto.fi)

    A postdoc position in the distributed computation group, with a focus on both development methods and tools for massively parallel systems as well as theory and analysis methods for ensuring their correctness.

  2. Deep learning(contact: Prof Juha Karhunen, juha.karhunen@aalto.fi)

    Deep learning is currently a hot topic in machine learning, because it can provide excellent results in difficult problems. Its learning algorithms are however difficult to use and quite sensitive to the choice of parameters. In this project we study improved deep learning methods with application to various data sets. For more information, see our web page http://research.ics.tkk.fi/bayes/research

  3. Machine learning in human computer interaction (contact: Prof Samuel Kaski, samuel.kaski@aalto.fi)

    Advanced machine learning techniques for human computer interaction are studied to facilitate intelligent information access. The grand challenge is to make use of massive interrelated and contextual information sources and select what information to present to the user in his current information need.

  4. Statistical data analysis for biomarker discovery and disease prediction (contact: Prof Harri Lähdesmäki, harri.lahdesmaki@aalto.fi)

    As part of a new Academy of Finland Centre of Excellence (SyMMyS), our research group works in a close collaboration with biology/clinical/immunology researchers in order to identify novel biomarkers for type 1 diabetes (T1D) and other diseases. Using extremely large-scale time-course data from unique clinical samples, the goal is to develop and apply novel computational biology/statistical modelling/machine learning methods to identify early (causative) biomarkers during the T1D pathogenesis. The data include various state-of-the-art genome-wide, -omics and clinical measurements. The postdoc will be responsible for developing efficient and sophisticated computational methods and collaborate with experimental research groups in a truly interdisciplinary setting. For more information, see: http://users.ics.tkk.fi/harrila/research/

  5. Computational biology and regulatory genomics (contact: Prof Harri Lähdesmäki, harri.lahdesmaki@aalto.fi)

    As part of a new interdisciplinary Academy of Finland Centre of Excellence (SyMMyS), we develop computational biology and statistical data analysis methods to understand molecular control mechanisms of regulation of T cell differentiation in human. These processes are analyzed at multiple levels using cutting-edge genome-wide technologies, including e.g. a variety of epigenetic modifications (ChIP-seq), protein-DNA interactions (ChIP-seq) and transcriptome expression (RNA-seq). Postdoc will be responsible for developing and applying efficient and sophisticated computational biology methods and collaborate with experimental and molecular biology research groups in a truly interdisciplinary setting. For more information, see: http://users.ics.tkk.fi/harrila/research/

  6. Symmetric key cryptanalysis (contact: Prof Kaisa Nyberg, kaisa.nyberg@aalto.fi)

    New extensions of linear and differential cryptanalysis methods for symmetric key ciphers will be investigated with special attention to distribution based distinguishers. The general goal would be to develop new and more accurate design criteria for symmetric key ciphers and their key scheduling algorithms. The specific research goals can be adjusted according to the interests of the postdoc.

  7. Kernel-based learning with multiple outputs, views and models (contact: Prof Juho Rousu, juho.rousu@aalto.fi )

    The goal is to develop new kernel-based machine learning methods for multiple and structured outputs, multiple kernels and ensemble models. In this project we propose to develop machine learning methods and tools that tackle the above problems via synthesis of multi-view learning, where the input data consists of several statistically dependent data sources, multi-task learning, where output consists of several statistically dependent targets, or tasks, and ensemble learning, where multiple models are combined to form an overall prediction. Methods will be built to be robust towards missing input and output data.

  8. Pattern discovery in deep biosphere (contact: Prof Juho Rousu, juho.rousu@aalto.fi )

    Life extends to several kilometers inside the earth crust, but what is it like? We try to find answers to this question by analysing the massive data from the microbial communities in deep bedrock groundwaters with bioinformatics and machine learning techniques.

  9. Multi-scale modelling of ecosystems (contact: Prof Juho Rousu and Dr Jaakko Hollmén,juho.rousu@aalto.fi and jaakko.hollmen@aalto.fi)

    The environment can be measured in many ways on different scales ranging from remote-sensing based satellite images of landscapes, to geological measurements, and to the chemical compositions of nutrients in individual organisms. Ecosystems exhibit complex interactions in both the spatial and temporal domains across different scales. Mining for compact patterns from this data is challenging and increasingly important.

  10. Statistical physics and computer science (contact: Prof Erik Aurell, erik.aurell@aalto.fi)

    A joint postdoc position in the group of Erik Aurell and the group of Mikko Alava (Aalto Applied Physics) focused on problems on the interface between computer science and statistical physics. The project is to work on either reconstructing dynamics in the framework of the "kinetic inverse Ising problem" or on using methods of statistical physics to analyse local search in large combinatorial optimisation or satisfiability problems, or on applying the modern theory on non-equilibrium fluctuations in new domains.

  11. Speech and language processing (contact: Dr Mikko Kurimo, mikko.kurimo@aalto.fi)

    The research group in speech and language processing has long traditions at the Department. The group focuses on computational models that can adapt to the large variation of everyday speech and language. This work has many interesting and important applications varying from speech recognition, synthesis, and retrieval to translation. For more information, see: http://research.ics.tkk.fi/speech/

Application procedure

The applications should be sent by email to registry@aalto.fi and they should contain the following documents in pdf format:

The candidate should also arrange for reference letters from the two indicated senior academics to be sent separately by email to registry@aalto.fi within two weeks of submission of the application.

The salary level for a starting postdoctoral researcher at the Aalto ICS Department is typically between 3200 and 3600 euros per month, depending on experience and qualifications. The contract period is usually for two years. The contract includes occupational health services and Finland has a comprehensive social security system. In addition to research work, a postdoctoral researcher is expected to participate in the supervision of students and teaching related to their research topic. Candidates may be invited for an interview on the Otaniemi campus of Aalto University in Espoo/Helsinki. In the review process, particular emphasis is put on the quality of the candidate's previous research and international experience, together with the substance, innovativeness, and feasibility of the research plan, and its relevance to the Department's mission. Good command of English is a necessary prerequisite. The candidate must have completed their PhD degree before the start of the contract period, and efficient and successful completion of studies is considered an additional merit.

For further information, please contact:

About the Department of Information and Computer Science

In the recent Research Assessment Exercise covering all the 46 units of Aalto University, the Department of Information and Computer Science was one of two units achieving an almost perfect score of 24 out of 25, from review panels assessing the units on a scale of 1 to 5 in the five subareas of scientific quality, scientific impact, societal impact, research environment, and future potential. For further information about the evaluation, see http://www.aalto.fi/en/research/rae/ and especially http://www.aalto.fi/fi/research/rae/aalto_rae_2009_panel_reports.pdf" (pages 110-113) The Department has a web site at http://ics.aalto.fi

About Aalto University

Aalto University is a newly created research university resulting from the merger of three Finnish universities: Helsinki University of Tehnology TKK, the Helsinki School of Economics, and the University of Art and Design Helsinki. The new university was launched in January 2010, and opens up a new world of possibilities for multidisciplinary education and research. For further information, see www.aalto.fi/en.

Tietojenkäsittelytieteen tutkijatohtoreita (dl. 2.4.2012)

Aalto-yliopiston tietojenkäsittelytieteen laitoksen tutkimus ja opetus painottuvat tekniikan ja tieteen haastavien sovellusten tarvitsemiin edistyneisiin laskennallisiin menetelmiin. Laitoksella kehitetään tehokkaita tietojenkäsittelytekniikoita mm. suurten, moniulotteisten tietoaineistojen analysointiin sekä kompleksisten ohjelmisto- ja tietoverkkosovellusten mallintamiseen ja suunnitteluun.

Tietojenkäsittelytieteen laitos etsii tutkijatohtoreita laitoksen tutkimusaloilta, erityisesti alla kuvatuilta aihealueilta. Näiden aihealueiden lisäksi myös laitoksen muilla tutkimuksen fokusalueilla ansioituneita henkilöitä rohkaistaan hakemaan. Viimeinen hakupäivä on 2.4.2012. Tietojenkäsittelytieteen laitos saattaa tehdä työtarjouksia erityisen ansioituneille hakijoille jo ennen viimeistä hakupäivää.

Aiheet

  1. Distributed computing (yhteyshenkilö Prof Keijo Heljanko, keijo.heljanko@aalto.fi)

    A postdoc position in the distributed computation group, with a focus on both development methods and tools for massively parallel systems as well as theory and analysis methods for ensuring their correctness.

  2. Deep learning (yhteyshenkilö prof. Juha Karhunen, juha.karhunen@aalto.fi)

    Deep learning is currently a hot topic in machine learning, because it can provide excellent results in difficult problems. Its learning algorithms are however difficult to use and quite sensitive to the choice of parameters. In this project we study improved deep learning methods with application to various data sets. For more information, see our web page http://research.ics.tkk.fi/bayes/research

  3. Machine learning in human computer interaction (yhteyshenkilö prof. Samuel Kaski, samuel.kaski@aalto.fi)

    Advanced machine learning techniques for human computer interaction are studied to facilitate intelligent information access. The grand challenge is to make use of massive interrelated and contextual information sources and select what information to present to the user in his current information need.

  4. Statistical data analysis for biomarker discovery and disease prediction (yhteyshenkilö prof. Harri Lähdesmäki, harri.lahdesmaki@aalto.fi)

    As part of a new Academy of Finland Centre of Excellence (SyMMyS), our research group works in a close collaboration with biology/clinical/immunology researchers in order to identify novel biomarkers for type 1 diabetes (T1D) and other diseases. Using extremely large-scale time-course data from unique clinical samples, the goal is to develop and apply novel computational biology/statistical modelling/machine learning methods to identify early (causative) biomarkers during the T1D pathogenesis. The data include various state-of-the-art genome-wide, -omics and clinical measurements. The postdoc will be responsible for developing efficient and sophisticated computational methods and collaborate with experimental research groups in a truly interdisciplinary setting. For more information, see: http://users.ics.tkk.fi/harrila/research/

  5. Computational biology and regulatory genomics (yhteyshenkilö prof. Harri Lähdesmäki, harri.lahdesmaki@aalto.fi)

    As part of a new interdisciplinary Academy of Finland Centre of Excellence (SyMMyS), we develop computational biology and statistical data analysis methods to understand molecular control mechanisms of regulation of T cell differentiation in human. These processes are analyzed at multiple levels using cutting-edge genome-wide technologies, including e.g. a variety of epigenetic modifications (ChIP-seq), protein-DNA interactions (ChIP-seq) and transcriptome expression (RNA-seq). Postdoc will be responsible for developing and applying efficient and sophisticated computational biology methods and collaborate with experimental and molecular biology research groups in a truly interdisciplinary setting. For more information, see: http://users.ics.tkk.fi/harrila/research/

  6. Symmetric key cryptanalysis (yhteyshenkilö prof. Kaisa Nyberg, kaisa.nyberg@aalto.fi)

    New extensions of linear and differential cryptanalysis methods for symmetric key ciphers will be investigated with special attention to distribution based distinguishers. The general goal would be to develop new and more accurate design criteria for symmetric key ciphers and their key scheduling algorithms. The specific research goals can be adjusted according to the interests of the postdoc.

  7. Kernel-based learning with multiple outputs, views and models (yhteyshenkilö prof. Juho Rousu, juho.rousu@aalto.fi )

    The goal is to develop new kernel-based machine learning methods for multiple and structured outputs, multiple kernels and ensemble models. In this project we propose to develop machine learning methods and tools that tackle the above problems via synthesis of multi-view learning, where the input data consists of several statistically dependent data sources, multi-task learning, where output consists of several statistically dependent targets, or tasks, and ensemble learning, where multiple models are combined to form an overall prediction. Methods will be built to be robust towards missing input and output data.

  8. Pattern discovery in deep biosphere (yhteyshenkilö prof. Juho Rousu, juho.rousu@aalto.fi )

    Life extends to several kilometers inside the earth crust, but what is it like? We try to find answers to this question by analysing the massive data from the microbial communities in deep bedrock groundwaters with bioinformatics and machine learning techniques.

  9. Multi-scale modelling of ecosystems (yhteyshenkilöt prof. Juho Rousu and johtava tutkija Jaakko Hollmén,juho.rousu@aalto.fi and jaakko.hollmen@aalto.fi)

    The environment can be measured in many ways on different scales ranging from remote-sensing based satellite images of landscapes, to geological measurements, and to the chemical compositions of nutrients in individual organisms. Ecosystems exhibit complex interactions in both the spatial and temporal domains across different scales. Mining for compact patterns from this data is challenging and increasingly important.

  10. Statistical physics and computer science (yhteyshenkilö prof. Erik Aurell, erik.aurell@aalto.fi)

    A joint postdoc position in the group of Erik Aurell and the group of Mikko Alava (Aalto Applied Physics) focused on problems on the interface between computer science and statistical physics. The project is to work on either reconstructing dynamics in the framework of the "kinetic inverse Ising problem" or on using methods of statistical physics to analyse local search in large combinatorial optimisation or satisfiability problems, or on applying the modern theory on non-equilibrium fluctuations in new domains.

  11. Speech and language processing (yhteyshenkilö johtava tutkija Mikko Kurimo, mikko.kurimo@aalto.fi)

    The research group in speech and language processing has long traditions at the Department. The group focuses on computational models that can adapt to the large variation of everyday speech and language. This work has many interesting and important applications varying from speech recognition, synthesis, and retrieval to translation. For more information, see: http://research.ics.tkk.fi/speech/

Hakuprosessi

Hakemus liitteineen tulee toimittaa Aalto-yliopiston kirjaamoon kirjaamo@aalto.fi. Hakemukseen tulee liittää ainakin seuraavat pdf-muotoiset dokumentit::

Hakijoiden on myös pyydettävä kaksi suosittelukirjettä yllä mainituilta suosittelijoilta. Ne lähetetään erikseen kirjaamoon ( kirjaamo@aalto.fi) viimeistään kaksi viikkoa viimeisestä hakupäivästä. Kaikki hakemusasiakirjat tulee toimittaa englanninkielisinä.

Aloittelevan tutkijatohtorin palkka tietojenkäsittelytieteen laitoksella on yleensä noin 3200-3600 euroa / kk pätevyydestä ja kokemuksesta riippuen. Sopimuskausi on yleensä kaksi vuotta. Sopimukseen sisältyvät työterveysetuudet. Tutkimustyön lisäksi valittujen henkilöiden odotetaan osallistuvan opetukseen ja opiskelijoiden ohjaamiseen hakijan tutkimusalueisiin liittyvissä aihepiireissä. Lyhytlistatut hakijat saatetaan kutsua haastatteluun Otaniemeen. Arviointiprosessissa painotetaan erityisesti hakijoiden aiemman tutkimustyön laatua ja heidän kansainvälistä kokemustaan sekä tutkimussuunnitelman sisältöä, innovatiivisuutta ja toteuttamiskelpoisuutta ja sen yhteensopivuutta laitoksen fokusalueiden kanssa. Hyvä englannin kielen taito on välttämätön. Hakijoilla tulee olla tohtorin tutkinto valmiina ennen työsopimuksen aloituspäivää. Tehokkaasti ja menestyksellisesti suoritetut jatko-opinnot nähdään lisäansiona.

Lisätietoja hausta antavat:

Tietojenkäsittelytieteen laitos

Vuonna 2009 toteutetussa, kaikki Aalto-yliopiston 46 akateemista yksikköä kattaneessa kansainvälisessä tutkimuksen arvioinnissa http://www.aalto.fi/en/research/rae/, erityisesti sivut 110-113 http://www.aalto.fi/fi/research/rae/aalto_rae_2009_panel_reports.pdf".

Aalto-yliopisto

Aalto-yliopisto on suomalaisille vahvuuksille rakentuva kansainvälinen yliopisto, jonka muodostavat arvostetut ja perinteikkäät korkeakoulut teknillistieteellisellä, kauppatieteellisellä ja taideteollisuuden alalla. Aalto-yliopisto hyödyntää aktiivisesti monitieteistä ja monitaiteista luonnettaan. Tavoitteena on kuulua maailmanluokan yliopistojen joukkoon vuoteen 2020 mennessä. Perus- ja jatko-opiskelijoita uudessa yliopistossa on 20 000 ja henkilöstöä 4 500, joista professoreja noin 300. Alumneja on yhteensä noin 75 000.