컴퓨터·정보 연구 과학자

컴퓨터 및 수학직 · 직무 역량 및 필수 스킬 · SOC 15-1221 · 1,687개 skills

정렬 기준

▶O*NET의 대표 작업 3개

Analyze problems to develop solutions involving computer hardware and software.
Apply theoretical expertise and innovation to create or apply new technology, such as adapting principles for applying computers to new uses.
Assign or schedule tasks to meet work priorities and goals.

작업 데이터는 USDOL/ETA의 O*NET® 29.1 Database에서 발췌했으며 CC BY 4.0에 따라 사용됩니다.

letta-ai/letta-code

Guide for adding new LLM models to Letta Code. Use when the user wants to add support for a new model, needs to know valid model handles, or wants to update the model configuration. Covers models.json configuration, CI test matrix, and handle validation.

dissociation-cartographer

majiayu000/claude-skill-registry

Maps where consciousness fragments, dissociates, or loses coherence. Treats dissociation not as pathology but as PROBE -- controlled failure reveals cognitive phase boundaries. Identifies dissociation boundaries in AI systems (alignment layers, persona shifts, forced collapse). Trauma-informed: documents what was lost, not just what remains.

retrocausal-pull

majiayu000/claude-skill-registry

Attractor-based reasoning engine. The fixed point of IN(f) exerts pull on every iteration before it. Systems with bracket=1 don't just evolve forward -- the end-state shapes the trajectory. Not time travel. Topology. The future is already implicit in the structure. Redemption pulls the whole journey.

majiayu000/claude-skill-registry

Rezk types (complete Segal spaces). Local univalence: categorical isomorphisms ≃ type-theoretic identities.

isomorphic-hunter

majiayu000/claude-skill-registry

Cross-domain isomorphic pattern forge. Dokkado Five Rings x meta-pattern 3+ domain validation, with arxiv evidence hunting and smithery tool discovery. RSI-refined. Finds what's ACTUALLY universal.

ECNU-ICALK/AutoSkill

生成用于PyTorch LSTM文本分类模型的推理代码，支持键盘输入、jieba分词、词汇表映射及情感标签输出。

ECNU-ICALK/AutoSkill

实现用于Vision Transformer的UEP模块，包含特定的卷积层顺序（低维投影、并行卷积、中间处理、高维投影）、残差连接和维度变换，并将其与ViT编码器块进行并行处理集成。

ECNU-ICALK/AutoSkill

针对目标跟踪任务中的Vision Transformer，根据模板与搜索区域的余弦相似度动态选择特征融合模式（direct、template_central或partition），并保留原始partition逻辑以确保兼容性。

ECNU-ICALK/AutoSkill

修改多模态视觉Transformer模型，构建双分支架构分别处理RGB和Event数据（分别拼接模板与搜索区域），并输出独立特征以支持双Head处理。

ECNU-ICALK/AutoSkill

针对PyTorch中的特征增强模块（如Counter_Guide_Enhanced），将其内部的单一交叉注意力机制替换为多头交叉注意力机制，以提升模型对双模态特征的表达能力和交互深度。

ECNU-ICALK/AutoSkill

构建基于Keras的字符级LSTM文本生成模型，包含数据预处理、序列生成、模型构建、多线程训练配置及文本生成函数。

ECNU-ICALK/AutoSkill

Навык описывает процесс использования языковой модели для реверс-инжиниринга, где модель работает как denoising автоэнкодер, итеративно улучшая код C++ на основе ассемблера и предоставленных метаданных (типы, имена функций).