Conputational Biologist | Cellzome, a GSK company, Heidelberg, Germany

August 2020 - Current

Spatial omics.

Senior research scientist | Heidelberg University, Germany

April 2018 - July 2020

Biomedical Computer Vision Group, PD Dr Karl Rohr

Deep Learning for Cancer Imaging Genomics.

Postdoctoral Associate | New York University, US

May 2015 - March 2018

Hochwagen Lab, Prof. Andreas Hochwagen, PhD

NGS data and Computational Biology methods to study meiosis. Design and implement data analysis pipelines in the Hochwagen lab. Design and maintain lab servers and website.

Postdoctoral Researcher | University of Lausanne, Switzerland

Nov 2010 - May 2015

Sanglard Lab, Prof. Dominique Sanglard, PhD

In vitro, ex vivo and in vivo infection models, NGS data and Computational Biology methods to study drug resistance and host-pathogenic yeast interaction.

Postdoctoral Researcher | University of Porto, Portugal

Feb 2007 - Nov 2010

Microbiology Lab, Prof. Eugénia Pinto, PhD

In vitro drug efficacy and toxicity models to discover and develop antifungal drugs from natural and synthetic sources.

Staff Pharmacist | Porto, Portugal

Aug 2006 - Feb 2007

Serpa Pinto Community Pharmacy

Charles University, Czech Republic

2002 - 2006

PhD, PharmD (Prof. Vladimír Buchta, PhD)

University of Porto, Portugal

1996 - 2002

BSc and MSc in Pharmaceutical Sciences

Next-generation sequencing (NGS):

• Analysis pipelines for Illumina, Pacific Biosciences’ SMRT, and Oxford Nanopore seq.

Software and Programming Languages:

• Python (Biopython, numpy, scipy, pandas, scikit-learn, tensorflow)
• R (tidyverse, Bioconductor, caret)
• SQL
• Linux (shell scripting, High-performance cluster computing)

Wet-lab Biology:

• Molecular cloning and CRISPR-Cas9 genome editing
• PCR and qPCR
• DNA microarrays
• Southern, western and northern blotting
• FACS
• Immunofluorescence
• In vitro, ex vivo, and in vivo models of infection and drug efficacy / toxicity

Machine Learning:

• Supervised and unsupervised learning (classification, regression, clustering)
• Feature engineering
• Model evaluation
• Deep learning

Research project management:

• International and interdisciplinary research project design and leadership

Research communication:

• Extensive list of peer-reviewed papers and a book chapter
• Presentation of scientific research results at internal and international scientific meetings

Team management:

• Design, supervision, and teaching undergraduate and graduate-level classes and research projects