Hiring Graduate Students for Deep Learning Applications and Data Analysis
We are seeking highly motivated graduate students in Computer Science, Engineering, or Mathematics ( especially in the field of analysis related to optimization), to join our team for using deep learning models in creating applications and analyzing data. The ideal candidates will have a strong background in the following areas:
1. Probability and Statistics:
- Comprehensive understanding of probability theory, statistical inference, and stochastic processes.
2. Linear Algebra:
- Proficiency in vector spaces, matrix operations, eigenvalues, and eigenvectors.
3. Optimization:
- Knowledge of optimization techniques, including gradient descent and convex optimization.
4. Deep Learning Theories:
- Understanding the mathematics behind deep learning theories and familiarity with the structure of models.
5. Model Training and Fine-Tuning:
- Ability to retrain models and fine-tune the parameters of a pretrained model with new data.
Additional requirements:
Experience or coursework in machine learning or deep learning.
Proficiency in programming languages and tools commonly used in deep learning research (e.g., Python, TensorFlow, PyTorch).
Having practical applications in deep learning is valuable.
If you are passionate about applying mathematical principles to the creation of innovative applications and data analysis using deep learning, we encourage you to apply.
@mehrdadab
We are seeking highly motivated graduate students in Computer Science, Engineering, or Mathematics ( especially in the field of analysis related to optimization), to join our team for using deep learning models in creating applications and analyzing data. The ideal candidates will have a strong background in the following areas:
1. Probability and Statistics:
- Comprehensive understanding of probability theory, statistical inference, and stochastic processes.
2. Linear Algebra:
- Proficiency in vector spaces, matrix operations, eigenvalues, and eigenvectors.
3. Optimization:
- Knowledge of optimization techniques, including gradient descent and convex optimization.
4. Deep Learning Theories:
- Understanding the mathematics behind deep learning theories and familiarity with the structure of models.
5. Model Training and Fine-Tuning:
- Ability to retrain models and fine-tune the parameters of a pretrained model with new data.
Additional requirements:
Experience or coursework in machine learning or deep learning.
Proficiency in programming languages and tools commonly used in deep learning research (e.g., Python, TensorFlow, PyTorch).
Having practical applications in deep learning is valuable.
If you are passionate about applying mathematical principles to the creation of innovative applications and data analysis using deep learning, we encourage you to apply.
@mehrdadab
