Destinus este o companie europeană din domeniul aerospațial și al apărării, care dezvoltă sisteme autonome de zbor pentru aplicații civile și de apărare. Fondată în 2021, compania proiectează și produce tehnologii de zbor pentru mai multe intervale de viteză, inclusiv platforme hipersonice viitoare. Activitatea sa multidisciplinară reunește ingineria structurilor aeronautice, propulsia cu turboreactoare, software-ul de zbor, inteligența artificială și proiectarea completă a sistemelor. Pe măsură ce echipa Destinus se extinde, compania oferă oportunități de a contribui la dezvoltarea unor sisteme de aeronave autonome, eficiente și de mare viteză.
Machine Learning Researcher, Aviation Safety Certification
Research mathematical safety guarantees for autonomous-flight AI at Destinus and Daedalean, connecting statistical learning theory with EASA aviation certification frameworks.
Descriere
- Review and develop certification strategies for supervised machine learning.
- Align mathematical guarantees, technical arguments, and model evaluations with the EASA W-shaped AI certification process.
- Investigate statistical learning theory, generalization bounds, and guarantees for deep neural networks in changing real-world conditions.
- Use statistics and stochastic processes to model environmental uncertainty and sensor noise, and assess confidence in performance metrics.
- Assess research literature, mathematical proofs, and engineering assumptions with peer-review rigor.
- Develop technical justifications and logical arguments for safety audits and certification frameworks.
- Write reproducible Python code to test hypotheses, validate certification methods, and benchmark model safety frameworks.
Cerințe
- Bring a deep understanding of statistical learning theory, generalization bounds, and the mathematical foundations of deep neural networks.
- Have a strong foundation in advanced probability, statistical modeling, and stochastic processes.
- Be proficient in Python and able to write clear, reproducible code; basic coding skills can be learned on the job with strong theoretical foundations.
- Be able to conduct thorough literature reviews, identify gaps in current AI research, and translate academic concepts into engineering frameworks; this can be learned with a strong quantitative foundation.
- Have experience or ability in peer review, including critical assessment of mathematical proofs, algorithmic assumptions, and research papers.
- Be pursuing or have recently completed a Master's or PhD in Computer Science, Mathematics, Theoretical Statistics, Electrical Engineering, Physics, or a related quantitative research field.
- Use critical thinking and maintain healthy skepticism toward machine learning assumptions and empirical accuracy.
- Be exceptionally capable of building sound logical arguments and technical justifications.
- Be motivated to tackle mathematical problems spanning theoretical computer science, safety engineering, and real-world flight operations.