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Postdoctoral researcher positions in information and computer science (DL
2 April 2012)
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, [email protected])
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, [email protected])
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/deep.shtml
3. Machine learning in human computer interaction (contact: Prof Samuel Kaski,
[email protected])
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, [email protected])
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,
[email protected])
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, [email protected])
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, [email protected])
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,
[email protected])
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, [email protected] and [email protected])
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,
[email protected])
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,
[email protected])
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 [email protected] and they should contain the
following documents in pdf format:
An application letter that includes contact information of the applicant, the topics of
interest in ranked order from the list above (if applicable), as well as names and contact
information of two senior academics available for reference per e-mail.
A research statement of at most three pages outlining planned postdoctoral work, and
the applicant’s motivation for pursuing the research specifically at the Aalto ICS
Department.
A complete curriculum vitae describing education and employment history.
List of publications, with pointers to openly available online versions of at most three of
the most relevant publications.
Degree certificate of the PhD degree, including a transcript of the doctoral studies. In
case the doctoral degree is still pending, an up-to-date study transcript and a plan for
completion of the degree must be provided.
Bibliometric data on the applicant's publications as follows:
number of published journal articles,
number of other publications,
following indicators according to both http://scholar.google.com and
http://academic.research.microsoft.com: total number of citations, h-index, gindex.
The candidate should also arrange for reference letters from the two indicated senior academics
to be sent separately by email to [email protected] 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:
HR Coordinator, Mr Stefan Ehrstedt, [email protected] (application process,
practical arrangements)
Head of Department, Prof Pekka Orponen, [email protected] (departmental
information)
The topic-specific contact person (topic-specific information)
The full call text is available at http://dept.ics.tkk.fi/calls/postdoc_Mar2012/
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 new university created in 2010 from the merger of the Helsinki University of
Technology TKK, the Helsinki School of Economics, and the University of Art and Design
Helsinki. The University’s cornerstones are its strengths in education and research, with 20,000
basic degree and graduate students, and a staff of 4,500 of whom 300 are professors. For
further information, see http://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, [email protected])
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, [email protected])
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/deep.shtml
3. Machine learning in human computer interaction (yhteyshenkilö prof. Samuel
Kaski, [email protected])
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, [email protected])
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, [email protected])
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,
[email protected])
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, [email protected])
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,
[email protected])
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 ja johtava
tutkija Jaakko Hollmén, [email protected] ja [email protected])
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,
[email protected])
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,
[email protected])
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 [email protected]. Hakemukseen
tulee liittää ainakin seuraavat pdf-muotoiset dokumentit:
Hakukirje, jossa mainitaan ainakin seuraavat asiat: hakijan yhteystiedot, hakijan
kannalta kiinnostavat tutkimuksen aihealueet yllä esitetystä listasta (soveltuvin osin)
preferenssijärjestyksessä sekä kahden suosittelijaksi lupautuneen, tieteellisesti
ansioituneen tutkijan sähköposti-yhteystiedot.
Korkeintaan kolmen sivun mittainen alustava tutkimussuunnitelma, josta selviää myös
miksi tutkimustyö halutaan toteuttaa Aalto-yliopiston tietojenkäsittelytieteen laitoksella
CV, joka sisältää koulutus- ja työllisyyshistorian kuvauksen kokonaisuudessaan
Julkaisuluettelo, jossa linkit kolmeen tärkeimpään pdf-muotoiseen julkaisuun
Tohtorin tutkintotodistus, johon liittyy selvitys tohtorintutkintoon sisältyvistä jatkoopintosuorituksista. Jos tutkinto ei ole vielä valmis, ajantasainen opintorekisteriote sekä
suunnitelma tutkinnon suorittamisesta.
Hakijan julkaisutoimintaa koskevat seuraavat bibliometriset tiedot:
tieteellisissä aikakausilehdissä julkaistujen artikkelien lukumäärä,
muiden julkaisujen lukumäärä,
seuraavat indikaattorit sekä http://scholar.google.com että
http://academic.research.microsoft.com sivustojen mukaisesti: julkaisuviitteiden
lukumäärä yhteensä, h-indeksi ja g-indeksi.
Hakijoiden on myös pyydettävä kaksi suosittelukirjettä yllä mainituilta suosittelijoilta. Ne
lähetetään erikseen kirjaamoon ([email protected]) 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:
HR koordinattori Stefan Ehrstedt, [email protected] (hakemusprosessi, käytännön
järjestelyt)
Laitoksen johtaja, prof. Pekka Orponen, [email protected] (laitokseen liittyvät
kysymykset)
Tutkimusaihekohtainen yhteyshenkilö (aihekohtainen tieto)
Hakuteksti on kokonaisuudessaan saatavilla http://dept.ics.tkk.fi/calls/postdoc_Mar2012/
Tietojenkäsittelytieteen laitos
Vuonna 2009 toteutetussa, kaikki Aalto-yliopiston 46 akateemista yksikköä kattaneessa
kansainvälisessä tutkimuksen arvioinnissa tietojenkäsittelytieteen laitos tunnistettiin toiseksi
yliopiston kahdesta parhaiten menestyneestä yksiköstä (RAE-tulos 24/25) sekä yhdeksi
maailman viidestä parhaasta tutkimusalansa keskuksesta. Koko raportti "Striving for Excellence,
Aalto University Research Assessment Exercise 2009" on saatavilla osoitteesta
http://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.