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Martin Engqvist is currently working at EnginZyme as the Group Lead for Computational Enzyme Design, beginning in June 2022. Prior to this role, they served as a Senior Computational Enzyme Engineer at EnginZyme, where they developed machine learning models and applied them to engineer industrially relevant enzymes. Martin also monitored research papers for new ML approaches and collaborated with experimentalists to facilitate the integration of computational predictions and in-lab enzyme tests.

Before joining EnginZyme, Martin worked at Chalmers University of Technology as an Assistant Professor from March 2017 to February 2022. In this role, they supervised and taught students at various academic levels and utilized machine learning and deep learning to build models for predicting protein thermostability. Martin also conducted bioinformatic studies on enzyme function. Martin briefly held the position of Docent in Computational Biology at Chalmers University in May 2021.

Prior to their time at Chalmers, Martin worked as a Senior Researcher at Sahlgrenska Academy at the University of Gothenburg from September 2016 to February 2017. Here, they designed and implemented a computational pipeline for processing next-gen sequencing data, which led to novel scientific insights and academic publications.

Martin also worked at CropDesign - a BASF Plant Science Company - as a Senior Scientist from March 2015 to June 2016. During this time, they integrated a large number of proprietary datasets to improve crop yield. Martin collaborated with multinational remote teams on various projects.

Martin's career began as a Postdoctoral Research Scholar at Chalmers University of Technology from June 2014 to February 2015. In this position, they conducted web scraping and data mining to collect growth temperature annotations for thousands of microorganisms. Martin then integrated this dataset with genomic and phylogenic data to gain valuable insights into microbial biology.

Prior to that, Martin worked as a Postdoctoral Research Scholar at Caltech from February 2011 to April 2014, where they conducted research on the directed evolution of rhodopsins. Martin worked alongside fellow researchers to overcome challenges and successfully complete the project.

Martin's earliest experience was as a Postdoctoral Research Scholar at the University of Cologne from June 2010 to December 2010. Here, they conducted final experiments and wrote research projects for publication. Martin also authored grant applications to secure a postdoctoral research fellowship.

Martin Engqvist has a strong education background in the field of biology. Martin obtained a Master's degree in Molecular Biology from Lund University from 2002 to 2006. Following that, they pursued their Doctor of Philosophy (Ph.D.) degree in Botany/Plant Biology from the University of Cologne, completing it in 2010. Additionally, Martin Engqvist engaged in research at the Max Planck Institute for Plant Breeding Research from 2006 to 2009, further specializing in Botany/Plant Biology.

In terms of additional certifications, Martin Engqvist has completed various relevant courses through Coursera and edX. These courses include topics such as Generative Adversarial Networks (GANs), Convolutional Neural Networks, Deep Learning, Machine Learning, and R programming. These certifications were obtained between 2014 and 2021, with the most recent achievement being the completion of multiple GAN-related courses in February 2021.