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Sven Giese is listed as Machine Learning Researcher at Bayer, a company with 98602 employees, based in Berlin, Berlin, Germany. AeroLeads shows a matched LinkedIn profile for Sven Giese.
Sven Giese previously worked as Wissenschaftlicher Mitarbeiter at Hpi and Wissenschaftlicher Mitarbeiter at Technische Universität Berlin. Sven Giese holds Master, Bioinformatics from Freie Universität Berlin.
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Tools. Biology. Data. Improvement: Making sense of data is challenging. Fortunately, talking to data and training machine learning models is what I love. Applied statistics and machine learning are the tools of choice to improve our understanding (of biology). Interrogating data with the right question allows me to solve the most challenging problems in data science and bioinformatics.Experienced in the field of language models, bioinformatics, machine learning, molecular design / protein design and solving machine learning tasks across modalities: small molecules, peptides, proteins, protein-complexes, protein expression patterns.Always looking to improve and hold-up to the highest engineering standards in our field and enjoying the communication with wet-lab experts and stakeholders.
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Berlin, Deutschland
Driving machine learning-guided optimisation of modalities, robust software engineering, working with a plethora of data sources (NGS, proteomics, biochemical assays), machine learning tools (NLP, classical ML, deep learning, applied statistics, visualisation) leveraging various molecule representations (structure, smiles, embeddings).#bioinformatics #lead.
(PhD) Computational analysis of (cross-linking) mass spectrometry data, applying statistics and machine learning in python (scikit-learn/tensorflow/keras).
Edinburgh, Großbritannien
Berlin Und Umgebung, Deutschland
Analyzing next-generation sequencing data (NGS) and mass spectrometry (MS) data from high-throughput experiments.
Berlin Und Umgebung, Deutschland
Working on my M. Sc. thesis "Quantitative Analysis of the Human Cell Surface Proteome", which was eventually published in The Journal of Proteome Research under the title "Phosphoproteomic Analysis of Aurora Kinase Inhibition in Monopolar Cytokinesis.".
Istanbul, Türkei
Berlin Und Umgebung, Deutschland
Developing a statistical method to estimate false positives in Next-Generation Sequencing, eventually published as "Specificity control for read alignments using an artificial reference genome-guided false discovery rate." in Bioinformatics.
Berlin Und Umgebung, Deutschland
Working on my B. Sc. thesis which was eventually published as book chapter in Statistical Analysis in Proteomics ("Detection of Unknown Amino Acid Substitutions Using Error-Tolerant Database Search.").
Other employees you can reach at bayer.com. View company contacts for 98602 employees →
Leonardo Nahuel Martín
Colleague at BayerArgentina, Argentina
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SP
Sandrine. Picault
Colleague at BayerSerrières-en-Chautagne, Auvergne-Rhône-Alpes, France, France
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AD
Anke De Landtsheer
Colleague at BayerBrussels Metropolitan Area, Belgium
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PF
Petr Foit M.D.
Colleague at BayerPrague, Czechia, Czech Republic
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HK
Horst Kleinwechter
Colleague at BayerBasel, Basel, Switzerland, Switzerland
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YG
Yavuz Gez
Colleague at BayerAnkara, Türkiye, Turkey
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WM
Werner Magnus Schmid
Colleague at BayerKöttmannsdorf, Carinthia, Austria, Austria
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DM
Daniel Morris
Colleague at BayerUnited Kingdom, United Kingdom
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GF
George Fang
Colleague at BayerGuangzhou, Guangdong, China, China
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AP
Ana Paula Mezzalira
Colleague at BayerCampo Novo Do Parecis, Mato Grosso, Brazil, Brazil
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Quick answers generated from the profile data available on this page.
Sven Giese works for Bayer.
Sven Giese is listed as Machine Learning Researcher at Bayer.
Sven Giese is based in Berlin, Berlin, Germany while working with Bayer.
Sven Giese has worked for Bayer, Hpi, Technische Universität Berlin, The University Of Edinburgh, and Robert Koch Institute.
Sven Giese's colleagues at Bayer include Leonardo Nahuel Martín, Sandrine. Picault, Anke De Landtsheer, Petr Foit M.D., and Horst Kleinwechter.
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Sven Giese holds Master, Bioinformatics from Freie Universität Berlin.
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