Isomorphic Labs - Senior Research Investigator (Biologics Computational Science), London
Requirements
• Essential • Essential • Experience using computational and machine learning methods to predict biologics properties. • Experience developing computational methods to predict biologics properties from sequence, such as affinity, developability or immunogenicity. • PhD or equivalent in computer science, computational biology, structural biology, biochemistry or related field. • Proficient programming skills with a strong emphasis on Python. • Experience preparing datasets, benchmarking models, and working with structure-based metrics. • Comfortable learning new ways of working in fast changing environments • Industry experience in a pharma or biotech environment. • Experience working in multidisciplinary large molecule drug discovery teams. • Experience with structural modelling of biologics, including 3D protein structure tools. • Experience using generative-design methods for biologics. • Experience working with complex biologics modalities, such as antibody-drug conjugates (ADC’s) or multispecifics. • Culture and values • Culture and values • We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. • ThoughtfulThoughtful at Iso is about curiosity, creativity and care. It is about good people doing good, rigorous and future-making science every single day. • Thoughtful • BraveBrave at Iso is about fearlessness, but it’s also about initiative and integrity. The scale of the challenge demands nothing less. • Brave • DeterminedDetermined at Iso is the way we pursue our goal. It’s a confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we. • Determined • TogetherTogether at Iso is about connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere. • Together • Creating an extraordinary company • We believe that to be successful we need a team with a range of skills and talents. We're building an environment where collaboration is fundamental, learning is shared and every employee feels supported and able to thrive. We value unique experiences, knowledge, backgrounds, and perspectives, and harness these qualities to create extraordinary impact. • We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. • Hybrid working • Hybrid working • It’s hugely important for us to share knowledge and build strong relationships with each other, and we find it easier to do this if we spend time together in person. This is why we follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on which team you’re in). If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for an initial screening call. • would require you to be able to come into the office 3 days a week • Please note that when you submit an application, your data will be processed in line with our privacy policy. • >> Click to view other open roles at Isomorphic Labs
Responsibilities
• Identify opportunities for computational modelling to enhance biologics discovery programs, including developing new methods. • Collaborate with machine learning and biologics discovery teams to develop computational and machine learning methods for predicting biologics properties from sequence, including affinity, developability and immunogenicity. • Benchmark existing computational methods and develop baseline models. • Analyse data from lab experiments to inform decisions for biologics discovery programs. • Determine what data to generate to improve machine learning models. Implement model-aware prioritization to ensure we are collecting the highest-value data points, laying the foundations for active learning. • Develop methods for predicting functional properties of biologics, expressed in terms of biophysical constraints.
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