[Please note updated start date information.]
We have openings for three full-time positions as well as a part-time M.Sc. student position.
1. Post-doc/Ph.D. studentship in computational systems biology
2. Ph.D. studentship in computational systems biology
3. Ph.D. studentship in statistical modeling and machine learning for next-generation sequencing data in systems biology
4. Part-time research assistant (MSc student level) in bioinformatics/computational systems biology
1. Post-doc/Ph.D. studentship in computational systems biology
The Computational Systems Biology
group led by Harri Lähdesmäki in the Department of Information and
Computer Science at Aalto University School of Science and Technology
(formerly Helsinki University of Technology) uses computational and
statistical modeling techniques to study molecular regulatory
mechanisms and biomolecular networks, and their role in health and
disease. We have interest in statistics, computational science and
molecular biology, and how modern computational methods can be applied
to experimental data to understand biological systems.
We have an opening for Post-doc or Ph.D. student level position in
developing and applying computational methods to model combinatorial
gene regulation by transcription factors, chromatin structure and other
epigenetics factors. This fascinating project is carried out in a very
close collaboration with national and international molecular biology
collaborators. The central aim is to develop modeling and
statistical data analysis methods to understand transcriptional and
post-transcriptional regulation at multiple levels, using
transcriptomics time course profiles and several next-generation
sequencing measurements, such as RNA-seq data, ChIP-seq measurements of
protein-DNA binding sites and epigenetics modifications, and other
measurements and database information.
Qualified candidates should have a Ph.D. (or M.Sc. for the Ph.D.
studentship) in computational biology, bioinformatics, computational
science, statistics or related fields, programming skills, strong
computational background, and genuine interest in molecular systems
biology. Soon graduating applicants can also be considered. For
Post-doc position, candidates need to demonstrate computational systems
biology proficiency shown by peer-reviewed publications.
Postdoc: initial appointment for one year, which can be extended by 2 years. Ph.D. studentship: initial appointment for 3 years.
Please send your application (or
informal queries) to Prof. Harri Lähdesmäki (harri.lahdesmaki@hut.fi).
For Post-doc position, please attach your CV, publication list and
contact information for three references. For Ph.D. studentship, attach
CV, publication list and transcript of your M.Sc. studies. Application
deadline: November 19, 2010. Start date: January 1, 2011 or later.
More information: http://www.cis.hut.fi/harrila/
2. Ph.D. studentship in computational systems biology
The Computational Systems Biology
group led by Harri Lähdesmäki, Department of Information and Computer Science, Aalto University
School of Science and Technology (formerly known as Helsinki University
of Technology)
We have an opening for Ph.D. student
level position in developing probabilistic and dynamic modeling methods
for protein signaling pathways and gene regulatory networks. The
project is part of a large interdisciplinary European research project
on "Systems biology of T cell activation", SYBILLA (http://www.sybilla-t-cell.de/)
which our group joined recently, as well as linked to EraSysBio+
project on "Signalling pathways and gene regulatory networks
responsible for Th17 cell differentiation", LymphoSys (http://www.erasysbio.net/index.php?index=280).
SYBILLA consortium develops new analytical and mathematical tools to
generate and integrate high-density quantitative data describing T-cell
activation. The goal of the Ph.D. student is to develop and apply
dynamical systems and statistical inference methods model signaling
networks using quantitative phospho-proteomics data and prior
biological knowledge. Modeling also includes connecting signaling
networks with transcriptional responses/networks using experimental
(deep-sequencing and array-data) and computationally-derived
information. Together with biology and biotechnology collaborators,
network modeling methods will be actively applied to quantitative
proteomics and transcriptomics data, experimentally validated, and
iterative improved via repetitive modeling and validation.
Qualified candidates should have
M.Sc. degree in computational biology, bioinformatics, bioinformation
technology, computational science, statistics, machine learning,
physics, or related fields, programming skills, strong computational
background, and genuine interest in molecular systems biology (prior
biological knowledge is beneficial but not a requirement). Soon
graduating applicants can also be considered.
Initial appointment is for 3 years.
Please send your application (or
informal queries) to Harri Lähdesmäki (harri.lahdesmaki@hut.fi).
Please attach your CV, publication list, transcript of your M.Sc.
studies, and contact information for references (optional). Application
deadline: November 19, 2010 (latest). Start date: available immediately.
More information: http://www.cis.hut.fi/harrila/
3. Ph.D. studentship in statistical modeling and machine learning for next-generation sequencing data in systems biology
The Computational Systems Biology
group, Department of Information and Computer Science, Aalto University
School of Science and Technology (formerly known as Helsinki University
of Technology)
We have an opening for Ph.D. student
level position in developing (1) computational and statistical modeling
methods for next-generation sequencing data and/or (2) statistical
modeling methods for molecular immunology, functional cancer genomics
and type 1 diabetes studies. High-throughput sequencing measurements,
such Solexa, Solid and Helicos, are revolutionizing systems biology
research and produce massive amounts of data that call for
sophisticated probabilistic analysis methods and highly efficient
algorithms in order to make meaningful biological conclusions.
Applicant will develop probabilistic analysis pipelines
(pre-processing, normalization, statistical modeling and testing) for
several types of deep-sequencing data, particularly RNA-seq, miRNA-seq
and various ChIP-seq. Modeling problems include e.g. statistical
quantification of gene expression and transcript isoform levels,
detection of novel isoforms and mutations, enrichment of epigenetic
modifications, etc. Data analysis pipelines will be applied in a number
of interdisciplinary systems biology research projects, including
Academy of Finland and EraSysBio+ funded project on "Signalling
pathways and gene regulatory networks responsible for Th17 cell
differentiation", LymphoSys (http://www.erasysbio.net/index.php?index=280).
Applicant will also develop statistical methods to simultaneously
analyze multiple data types measured from Type 1 Diabetes samples to
generate hypothesis for molecular mechanisms underlying the disease.
Qualified candidates should have
M.Sc. degree in computational biology, bioinformatics, bioinformation
technology, computational science, statistics, machine learning,
physics, or related fields, programming skills, strong computational
background, and genuine interest in molecular systems biology (prior
biological knowledge is beneficial but not a requirement). Soon
graduating applicants can also be considered.
Initial appointment is for 3 years.
Please send your application (or
informal queries) to Harri Lähdesmäki (harri.lahdesmaki@hut.fi).
Please attach your CV, publication list, transcript of your M.Sc.
studies, and contact information for references (optional). Application
deadline: November 10, 2010. Start date: available immediately.
More information: http://www.cis.hut.fi/harrila/
4. Part-time research assistant (MSc student level) in bioinformatics/computational systems biology
The Computational Systems Biology
group, Department of Information and Computer Science, Aalto University
School of Science and Technology (formerly known as Helsinki University
of Technology)
Part-time research assistant can focus on any of the above research topics; please see the other job descriptions.
Qualified candidates should have
B.Sc. degree in computational biology, bioinformatics, bioinformation
technology, computational science, statistics, machine learning,
physics, or related fields, programming skills, computational skills,
and interest in molecular systems biology (prior biological knowledge
is beneficial but not a requirement).
Please send your application (or
informal queries) to Harri Lähdesmäki (harri.lahdesmaki@hut.fi).
Please attach your CV, publication list, transcript of your M.Sc.
studies, and contact information for references (optional). Application
deadline: November 30, 2010. Start date: flexible.
More information: http://www.cis.hut.fi/harrila/