Job Description :
Formulate predictive modeling and learning scenarios and apply cutting-edge machine learning (deep learning) approaches for data-driven decision making
Develop new approaches to open questions posed by colleagues of their processes and related datasets
Operationalize machine learning solutions by collaborating in the development of appropriate infrastructure and interfaces
The successful candidate will provide strong problem-solving skills and hands-on software engineering capability to implement a wide range of data and machine learning solutions.
Scenarios include working alongside experts in familiar applications of machine learning in the biotechnology domain, e.g., patient selection, bioinformatics, cheminformatics, molecular biology, biomedical informatics, Example application areas include:
Application of supervised, unsupervised, semi-supervised, reinforcement learning, and transfer learning methods to derive robust representations from small labeled and large unlabeled datasets.
Applications of convolutional neural networks for detection, segmentation, and classification of various biological and medical imaging data.
Applications of generative models in drug discovery and optimization.
Applications of NLP and Sequence models for disease prognosis, patient selection, and treatment optimization.
The position requires an individual with demonstrated engineering rigor and excellent communication and collaboration skills. Keen interest and hands-on expertise in the interdisciplinary application of advanced machine learning methods to biotechnology research scenarios are imperative.

Responsibilities include but are not limited to:

Development and maintenance of internal benchmarks on a range of machine learning problems, including image recognition, NLP, and cheminformatics.
Contribute to development and maintenance of machine learning data repository and curation platform.
Train, optimize, document, and present novel and existing deep learning solutions.
Contribute to broader data analysis and predictive methods strategies as required, including assessment of 3rd party capabilities.
Contribute to presentation and reporting of methods, results and conclusions to a publishable standard.

Background experience & complementary knowledge
Minimum MSc in Computer Science, Electrical Engineering, or related field.
At least two years of experience of applied machine learning preferably in university, hospital, biotechnology or technology research environment.
Proven experience in developing and applying novel algorithmic solutions.
In-depth knowledge of contemporary machine learning, pattern recognition and data-mining techniques, paradigms, and application scenarios.
Expertise in more than one of the following key areas:
o ANNs and Large-margin Classifiers, including CNN, RNN, and Attention Models.
o Generative Neural Network Models
o Active, Transfer & Semi-supervised learning
o Deep Reinforcement Learning
o Ensemble methods
Demonstrable commitment to rigorous practice in reproducible research.
Hands-on experience of data integration, mining & visualization.
Professional experience in working with contemporary data and computing infrastructures
o Proficient in TensorFlow, Numpy, Scipy
o Proficient in Python, Linux bash, Git, and GitHub
o Familiar with AWS, JavaScript, back-end, and front-end technologies
Proven problem-solving skills, collaborative nature, and adaptability across disciplines.
Excellent verbal and written communication skills, ability to convey complex subject matter to lay audiences. Fluent verbal and written English language skills prerequisite.