
Post- Doctoral Associate in the Division of Engineering - Dr Tuka Alhanai
- Abu Dhabi
- Permanent
- Full-time
- Human-Centered Applications: Familiarity applying ML in areas like healthcare, education, neuro-robotics, and/or assistive technologies. Prior experience in physiological signal processing (e.g., EMG, EEG, ECG) is an advantage. Familiarity with HCI principles and frameworks, in particular, experience conducting usability studies and designing user-centric AI systems.
- Assistive / Collaborative Robotics: Interest in developing robotic systems for rehabilitation, assistive technology, or neuro-prosthetics, leveraging machine learning to improve precision and adaptability in user interactions. Knowledge of deploying robots in shared workspaces, focusing on safety, cooperation, and efficiency in human-robot teams.
- Multi-Modal ML: Expertise in working with diverse data types, such as vision, speech, images, and physiological signals. Experience integrating multiple modalities to build robust AI systems is an advantage
- Interdisciplinary Applications: Leveraging LLM / VLMs for interdisciplinary problems, such as: AI-driven scientific discovery, automating hypothesis generation in finance / natural sciences / physical sciences, enhancing collaborative workflows in complex organizational settings.
- Applicants must have a PhD in Computer Science or related field.
- Experience in one or more ML domains, such as deep learning, reinforcement learning, or human-centered ML.
- Proficiency in programming languages (e.g., Python) and ML frameworks (e.g., TensorFlow, PyTorch), with evidence in the form of public (Github) repository.
- Excellent abilities in communication, teamwork, and mentorship.
- Strong publication record in top-tier conferences (e.g., NeurIPS, ICML, CHI, CVPR).
Times Higher Education