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Open nowPosted 2 days ago

Docente de Graduação - Deep Learning

CESAR15 open roles

Where
Recife, Pernambuco, Brasil
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Your applicationOpen nowDocente de Graduação - Deep LearningCESAR · Recife, Pernambuco, Brasil

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The clock on this job

Early applications get read.

13.8% of postings close within 7 days. Measured by our own scanner across the market. CESAR postings stay open a median of 15 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.8%7 days
  4. 22.3%14 days
  5. 37.8%30 days
  6. 59.0%60 days
This job: posted 2 days ago

CESAR median: 15 days open

The posting

Estamos em busca de um(a) Docente para a disciplina eletiva de Deep Learning na graduação em Sistemas de Informação.

Buscamos um(a) profissional com sólida base teórica e prática em Inteligência Artificial e Aprendizado Profundo, capaz de guiar os alunos desde os fundamentos de redes neurais até arquiteturas modernas de Visão Computacional, Processamento de Linguagem Natural (PLN), Transformers e Modelos de Grande Escala (LLMs / Foundation Models), com ênfase em implementação prática e resolução de problemas reais.

Responsabilidades e Atribuições

  • Ministrar a disciplina eletiva de Deep Learning, cumprindo a ementa e os objetivos pedagógicos do plano de ensino.
  • Conduzir aulas práticas e laboratoriais utilizando metodologias ativas, resolução de problemas e implementação de código (hands-on).
  • Orientar os estudantes na concepção, treinamento, ajuste fino (fine-tuning), avaliação e otimização de modelos de redes neurais profundas.
  • Discutir boas práticas de engenharia de IA, mitigação de overfitting, estratégias de regularização e aspectos éticos no uso de modelos de grande escala.
  • Acompanhar o progresso técnico dos estudantes por meio de projetos práticos e avaliações continuadas.
  • Participar de alinhamentos pedagógicos e eventos do corpo docente da CESAR School.

Requisitos

  • Formação Acadêmica:
  • Graduação concluída em áreas técnicas correlatas: Ciência da Computação, Sistemas de Informação, Engenharia da Computação, Engenharia de Software, Ciência de Dados, Estatística, Matemática Computacional, Física ou áreas afins. Titulação em nível de Mestrado e/ou Doutorado concluído (ou em fase final) nas áreas acima citadas ou correlatas à Inteligência Artificial/Computação Aplicada.

Conteúdo Programático / Ementa da Disciplina

  1. Introdução à Deep Learning: Panorama, evolução e aplicações contemporâneas.
  2. Conceitos Básicos de Redes Neurais: Perceptron, funções de ativação, algoritmos de otimização, regularização e mitigação de overfitting.
  3. Introdução à Visão Computacional: Processamento básico de imagens e tarefas fundamentais.
  4. Redes Neurais Convolucionais (CNNs): Arquiteturas clássicas e modernas (LeNet-5, AlexNet, VGG, ResNet).
  5. Introdução ao Processamento de Linguagem Natural (PLN): Pré-processamento textual e representação vetorial (Word Embeddings).
  6. Redes Neurais Recorrentes: RNNs, LSTMs e mecanismos de atenção.
  7. Arquitetura Transformers: Mecanismo de autoatenção e encoders/decoders.
  8. Modelos de Grande Escala: Arquiteturas generativas, foundation models e aplicações de ponta.
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