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PostDoc in Computational and Experimental Protein Design(m/w/x)
Developing computational methods for antibody engineering and drug discovery, integrating ML with Molecular Dynamics in a lab-in-the-loop setup. PhD in computational structural biology or related field, with deep ML understanding for protein prediction, required. Direct contribution to breakthrough drug discovery.
Anforderungen
- Solid understanding of protein biophysics
- Hands-on experience with core laboratory techniques for creating protein variants, such as protein expression and purification, or eagerness to learn
- PhD in a relevant field, such as Computational Structural Biology, Bioinformatics, Biophysics, Biochemistry, or a related field with a strong computational focus
- Deep understanding of machine learning approaches for protein structure and property prediction, and generative models
- Familiarity with structural modeling and design platforms for biomolecules and protein-protein interactions (e.g. MOE, Schrödinger, Rosetta, modeling and docking algorithms)
- Fluency in English and strong oral and written communication skills
- Solid understanding of antibody structure and/or familiarity with immune repertoire sequencing datasets
- Hands-on experience of in vitro testing methods for antibody characterization, such as Surface Plasmon Resonance (SPR)
- Engagement in scientific research continuously since PhD and readiness to start an RPF postdoctoral activity no later than 4 years after completing PhD
Aufgaben
- Develop and validate novel computational methods for antibody engineering
- Integrate Machine Learning with Molecular Dynamics workflows
- Implement a lab-in-the-loop approach for computational modeling and experimental validation
- Create a predictor for antibody affinity maturation and developability
- Contribute to drug discovery efforts for breakthrough medicines
- Attend and present at scientific meetings
- Interact with the scientific community and publish experimental advances
- Collaborate with host teams and stakeholders on protein design algorithms
Berufserfahrung
- ca. 4 - 6 Jahre
Ausbildung
- Doktor / Ph.D.
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- MOE
- Schrödinger
- Rosetta
- Surface Plasmon Resonance (SPR)
Noch nicht perfekt?
- Roche Diagnostics GmbHVollzeitnur vor OrtManagementPenzberg
- Roche Diagnostics GmbH
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VollzeitPraktikumnur vor OrtPenzbergab 2.268 / Monat - Roche Diagnostics GmbH
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PostDoc in Computational and Experimental Protein Design(m/w/x)
Developing computational methods for antibody engineering and drug discovery, integrating ML with Molecular Dynamics in a lab-in-the-loop setup. PhD in computational structural biology or related field, with deep ML understanding for protein prediction, required. Direct contribution to breakthrough drug discovery.
Anforderungen
- Solid understanding of protein biophysics
- Hands-on experience with core laboratory techniques for creating protein variants, such as protein expression and purification, or eagerness to learn
- PhD in a relevant field, such as Computational Structural Biology, Bioinformatics, Biophysics, Biochemistry, or a related field with a strong computational focus
- Deep understanding of machine learning approaches for protein structure and property prediction, and generative models
- Familiarity with structural modeling and design platforms for biomolecules and protein-protein interactions (e.g. MOE, Schrödinger, Rosetta, modeling and docking algorithms)
- Fluency in English and strong oral and written communication skills
- Solid understanding of antibody structure and/or familiarity with immune repertoire sequencing datasets
- Hands-on experience of in vitro testing methods for antibody characterization, such as Surface Plasmon Resonance (SPR)
- Engagement in scientific research continuously since PhD and readiness to start an RPF postdoctoral activity no later than 4 years after completing PhD
Aufgaben
- Develop and validate novel computational methods for antibody engineering
- Integrate Machine Learning with Molecular Dynamics workflows
- Implement a lab-in-the-loop approach for computational modeling and experimental validation
- Create a predictor for antibody affinity maturation and developability
- Contribute to drug discovery efforts for breakthrough medicines
- Attend and present at scientific meetings
- Interact with the scientific community and publish experimental advances
- Collaborate with host teams and stakeholders on protein design algorithms
Berufserfahrung
- ca. 4 - 6 Jahre
Ausbildung
- Doktor / Ph.D.
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- MOE
- Schrödinger
- Rosetta
- Surface Plasmon Resonance (SPR)
Über das Unternehmen
Roche Diagnostics GmbH
Branche
Pharmaceuticals
Beschreibung
Das Unternehmen setzt sich dafür ein, Krankheiten zu verhindern, zu stoppen und zu heilen, und gewährleistet den Zugang zur Gesundheitsversorgung.
Noch nicht perfekt?
- Roche Diagnostics GmbH
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Science and Matrix Lead in the environment of Advanced Cell-based Assays for Neuroscience Drug Discovery(m/w/x)
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Internship in Automation Solutions for Antibody Development(m/w/x)
VollzeitPraktikumnur vor OrtPenzbergab 2.268 / Monat - Roche Diagnostics GmbH
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