Paweł P Łabaj

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Paweł P Łabaj
Events attended

Małopolska Centre of Biotechnology of Jagiellonian University
ul. Gronostajowa 7A
30-387 Krakow


2017 –                    Bioinformatics Group Leader, Małopolska Center of Biotechnology, Jagiellonian University, Kraków, Poland

2015 – 2018           Research Fellow of Austrian Academy of Sciences, Personal scholarship of the Austrian Programme for Advanced Research and Technology (APART)

2015 –                    Guest Researcher affiliated at Chair of Bioinformatics RG, Department of Biotechnology, Boku University Vienna, Austria

2007 – 2015           Chair of Bioinformatics RG, Department of Biotechnology, Boku University Vienna, Austria
                                post-doctoral research fellow 2012 – 2015
                                pre-doctoral research fellow 2007 – 2012

2006 – 2007           Department of Biomedical Informatics, Institute of Medical Technology and Equipment, Zabrze, Poland
                                junior member of research team


Paweł Łabaj has studied Computer Science in Medicine at Silesian University of Technology (Gliwice, Poland). For his MSc thesis he was working on the project in The Institute of Medical Technology and Equipment (Zabrze, Poland) where he has developed system of automatic analysis and pattern recognition using neural networks applied in Fetal Heart Rate monitoring devices. He then has joined the Vienna Science Chair of Bioinformatics at Boku University Vienna, where he obtained PhD in Bioinformatics on Measurement and data analysis in the face of noise and complex backgrounds – Advances from improved bioinformatics algorithms. He was active member of FDA MAQC-III/SEQC consortium where he studied performance of the platforms and pipelines for high throughput expression profiling. This experience has led to winning the APART Fellowship of Austrian Academy of Sciences as well as recent competition for bioinformatics group leader at Malopolska Centre of Biotechnology (Krakow, Poland). Dr Łabaj’s research focuses on consequences of gene vs. alternative transcript expression profiling, as well as on the approaches for assessing performance / benchmarking of the platforms and analysis pipelines.