Sort by
Refine Your Search
-
Listed
-
Field
-
those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and LLMs: practical knowledge with Deep Learning architectures, and in particular, with
-
those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and LLMs: practical knowledge with Deep Learning architectures, and in particular, with
-
those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and LLMs: practical knowledge with Deep Learning architectures, and in particular, with
-
those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and LLMs: practical knowledge with Deep Learning architectures, and in particular, with
-
for applications is required, in the contracting phase, including those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and Large Language Models (LLMs
-
completed by the deadline for applications is required, in the contracting phase, including those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning
-
factors: In-depth knowledge of Deep Learning and Large Language Models (LLMs): practical knowledge with Deep Learning architectures, and in particular, with LLMs. Knowledge of both advanced prompt
-
processes. Preferred factors: Knowledge of Industrial and Systems Engineering Knowledge in preparing scientific articles in the field of sustainable electric mobility Knowledge in complex systems modeling and
-
the contracting phase, including those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and LLMs: practical knowledge with Deep Learning architectures
-
processes. Preferred factors: In-depth knowledge of Deep Learning and Large Language Models (LLMs): Practical knowledge with Deep Learning architectures, and in particular, with LLMs. Knowledge of both