[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/