진학프로석·박사 채용 접수 플랫폼

Your mission:

If you are passionate about research, this challenge is for you. This position is dedicated to developing research within the scope of LA/P/0079/2020 – the Associate Laboratory in Energy, Transport and Aerospace, funded by the Foundation for Science and Technology (FCT). It focuses on Scientific Artificial Intelligence, optimization, and computational modeling in engineering, aiming to accelerate technological maturity (TRL), validation, and the transfer of scientific results.

Main Responsibilities:

• • Application of advanced Machine Learning, Deep Learning, reduced-order models, and physics-informed/physics-guided models to complex engineering systems
• • Development and use of advanced scientific AI methodologies, including surrogate models, generative models, probabilistic models, and both data-driven and physics-informed learning techniques
• • Development of simulation-based optimization methodologies, including Bayesian optimization, derivative-free optimization, multi-objective optimization, model calibration, and parameter identification
• • Implementation of simulation, forecasting models, AI-accelerated computational models, and digital twins
• • Support for validation and technological maturation (TRL) of project results
• • Collaboration in multidisciplinary teams and across multiple parallel workstreams
• • Production of scientific publications, scientific software, computational tools, technical reports, and contributions to knowledge transfer

What is offered to you:

• • Participation in challenging and innovative projects with high impact and value creation for industry
• • Opportunities for personal and professional development in a stimulating, informal environment that encourages and values new ideas
• • Employment contract with salary conditions aligned with experience, qualifications, and role responsibility
• • Flexible working hours and a culture that values work-life balance
• • Health insurance and access to a network of partnerships and protocols focused on individual well-being
• • 22 days of vacation, plus 5 additional days per year, including your birthday

Minimum Requirements:

• • PhD in Engineering
• • Experience in Machine Learning, Deep Learning, reduced-order modeling, or physics-informed models
• • Knowledge of numerical simulation, computational modeling, optimization, or digital twins
• • Experience in simulation-based optimization, including Bayesian optimization, derivative-free optimization, model calibration, or parameter identification
• • Proficiency in Python and frameworks (e.g., PyTorch, TensorFlow)
• • Knowledge of HPC, scientific computing, cloud computing, MLOps, or scientific software development
• • Relevant scientific and technological output, including publications, scientific software, computational tools, technical reports, patents, or contributions to knowledge transfer
• • Experience applying AI, optimization, or computational modeling methodologies to case studies in energy, transport, aerospace, defense, resilient industry, materials, or structures
• • Critical thinking and results-oriented mindset
• • Excellent written and verbal communication skills
• • Ability to work in multidisciplinary environments and in teams
• • Autonomy and scientific rigor
• • Proficiency in English (spoken and written); knowledge of Portuguese is a plus